Management training simulation method and system

ABSTRACT

A management training simulation system and method are disclosed. A method in accordance with one aspect of the invention is implemented on a computer and develops the decision-making skills of a user in a defined, simulated situation which includes one or more firms controlled by participants in the simulation which cause particular object designs to be injected into the simulation. Each object design is defined through an attribute-characteristic representation. A multipeaked value function is used to process the designs throughout the simulation instead of a distance-value function as in conventional simulations. The participant is selectively provided with information about at least some of the objects in the simulation, preferably at a cost, as well as a valuation of those objects and is also apprized of the current state of his or her firm. The participant digests this information and creates revised object designs which are sent to the simulation and processed using the multipeaked value function. Information concerning the objects in the simulation from all firms is updated in view of the newly submitted object designs, and these steps are repeated until the simulation ends. A network preferably interconnects plural simulation participants to a central computer which runs the simulation.

This application claims priority pursuant to 35 U.S.C. Section 119 basedupon U.S. Provisional Application Ser. No. 60/094,900, filed Jul. 31,1998, and Ser. No. 60/141,738, filed Jun. 30, 1999, the disclosures ofwhich are hereby incorporated by reference in their entireties as if setforth herein.

FIELD OF INVENTION

The present invention relates generally to management trainingsimulations (MTSs), which are computer programs or board games that helpmanagers learn to manage and to understand business. More particularly,the present invention involves a computerized management training methodand system that effectively teaches the development and use of knowledgeand provides training in managing strategy, risk, innovation, and corecompetencies, as well as analyzing and correcting a manager's decisionmaking processes and identifying a manager's unique judgmental biasesand errors. It provides tailored, individualized training in managerialjudgment and decision making.

BACKGROUND OF THE INVENTION

MTSs are computer simulations that teach managers how to make betterinformed decisions. They present a manager with a lifelike situationsimulated by a computer. The manager endeavors to improve the situation.To do this, he analyzes the situation and responds with a decision.Using the model, the computer then calculates and displays theconsequences of his decision. If the simulation closely approximatesrealistic situations, the manager learns how to confront thosesituations when they arise in the work environment.

MTSs are also called business simulations, business gaming, and businesswar games. Many business schools, corporate universities, consultingfirms, training firms, and human resource departments use MTSs to teacha wide variety of subjects including marketing, finance, accounting,business strategy, supply chain management, and organization design.

There is a great need for this educational technology. People learn bestfrom practical, hands on experience. Yet the primary source of suchexperience, one's business, is a difficult place in which to learn.Business experiments are not repeatable, decision consequences representthe outcomes of many influences, and the penalties for failure arepotentially high. Business risks, costs, and complexity prevent amanager from engaging in the playful, mistake driven experimentationthrough which people learn best.

The predominant alternative to learning ‘on the job’ are books andclassroom study. These methods are also limited. Applying intellectualknowledge to practice is extremely difficult. For example, no medicalstudent is expected to move directly from Gray's Anatomy to surgery.

MTSs overcome the problems of learning ‘on the job’ and of classroomstudy. They are the ideal means for learning: experiments are repeatablewhile consequences are discernable and immediate. They condense years ofexperience into a few hours of study, thereby improving the learningthat managers gain from their most limited resource—time. MTSs bridgethe distance between intellectual understanding and practice (ascadavers do for medical students). They facilitate practical learningwithout risking “the patient”—one's career and company.

A manager will gain the following benefits by using MTSs to improve hismanagement skill:

One can test his own strategies and intuitions—the student directs thelesson, rather than the lesson directing the student (as in traditionalclassroom learning).

MTSs provide more realistic exercises than those found in books orlectures, while still being less complex than real life situations.

MTSs can isolate critical skills. Managers can concentrate on improvingthese skills without being encumbered by the complexity of the realtask.

The consequences of one's actions appear immediately and are easilydiscerned.

Unlike in one's actual job, there is no penalty for failure. One canexperiment risk free.

MTSs facilitate testing ideas before real life implementation (called“what if” experiments).

MTSs increase communication by instigating discussion of strategy andoperations and by illuminating business concerns.

FIG. 1 shows a most general architecture of an MTS. An MTS is composedof four parts: a display for presenting information about a simulatedbusiness situation (103); an input device for a person or team learningwith the MTS (hereafter called a student) to input decisions into theMTS (104); a simulation of a business situation (101); and a businesssimulation manipulator (102) for calculating and producing the effectsof students' decisions on the business situation. The arrows in FIG. 1represent the movement of information and decisions in the MTS. Themovement of information and decisions is best explained by describingthe operation of an MTS. This is as follows: The display gathersinformation from the simulated business situations and displays thisinformation for the students. After witnessing the information, thestudents make decisions. The students enter their decisions into thebusiness situation via an input device. Upon receiving the students'decisions, the business simulation manipulator calculates the effects ofthe students' decisions in the simulated business situation. Informationfrom the affected business situation is then displayed for the students.

An important class of MTS within the general MTS architecture depictedin FIG. 1 is the competitive industry MTS. In such MTSs the simulatedbusiness situation comprises a simulation of a competitive marketplace.Competitive industry MTSs teach the management of business functionswhere markets influence business results; for example, marketing,finance, and business strategy. For simplicity, I refer to competitiveindustry MTSs as MTSs and refer to the general case depicted in FIG. 1as the ‘general case’ MTS.

FIG. 2 shows the architecture of an MTS. The simulated businesssituation is a competitive industry. The simulated competitive industryis composed of at least two types of components: a marketplace model(201) and at least one firm (204) controlled by a student. Themarketplace model simulates, among other things, products, customers,market segments, and technology (described below). The marketplace modelinfluences the structure and dynamics of the simulated competitiveindustry. Each student manages a separate firm. Through their respectivefirms, students compete against each other for profits and market sharein the marketplace. Each firm has several characteristics relating tobusiness processes (for example, manufacturing capacity, the number ofsalespeople, operating capital, debt, and accounts receivable). Themarketplace model and firm model determine the decisions required ofstudents and the lessons learned. Depending upon the characteristics ofthe simulated marketplace and the simulated firms, MTSs might requirethat managers compete in several markets and/or manage one or more ofseveral business functions (for example, finance, marketing, sales,customer service, and research and development).

To manage their firm and, specifically, to receive information and inputdecisions, students use an interface (205). This interface is typicallyan integration of the display and input devices shown in FIG. 1.However, some business simulations are played as board games (for anexample see U.S. Pat. No. 5,056,792). In such board games, the firmmodel and market models are comprised of a visual display on the gameboard and a set of rules governing play and hence the display on theboard. For example, a portion of the game board might represent firms.Chips placed on this portion of the board represent the firm'scharacteristics, such as the amount of inventory. Rules determine whenchips are added or removed from the board. Another portion of the boardrepresents the marketplace in a similar manner. When an MTS is played asa board game, the interface is the game board itself. Making thisdistinction, one versed in the art will recognize that the generaldescriptions of MTS given throughout this document apply to both boardgames and computer simulation MTSs.

The arrows in FIG. 2 represent the movement of information, revenues,and decisions in the MTS. The movement of these objects is most clearlyexplained by describing the operation of an MTS.

Each application of an MTS is called a learning session. A learningsession progresses through rounds where each round consists of thefollowing sequenced steps:

1. Each interface collects information describing its student's firm andthe marketplace. The firm's characteristics constitute the informationdescribing the student's firm. Information about the marketplace mightinclude, for example, the products previously sent to the marketplace,the prices offered, sales volumes, and competitors' market shares. Eachinterface displays this information to its student.

2. Using the information presented by the interface, each studentdetermines his firm's decisions for the current round. These decisionsmight include, for example, pricing products, purchasing manufacturingcapacity, and producing products.

3. With an input means (for example, a keyboard or mouse) each studententers his decisions into the interface. The interface sends thesedecisions to the student's firm.

4. Each student's firm implements its student's decisions. The producedproducts are sent to the marketplace.

5. Having received the production from all the firms, the marketplacesimulates the sale of all firms' products. This simulation mightinclude, for example, evaluating firms' products and calculating demand.For these tasks, the marketplace model will contain a product evaluator(FIG. 2, field 203) for evaluating products and a market manipulator forcalculating demand (FIG. 2, field 202). After the sales are determined,the sales' revenues are sent to the appropriate firm. After completingthese five steps a round is complete. The next round begins with stepone.

The following description focuses upon marketplace models and productdesign to facilitate the discussion of MTSs in general. MTSs require amarketplace model which represents both products and markets. MTSs alsorequire students to perform three tasks: (1) analyze the marketplace andcompeting firms, (2) design products and set prices, and (3) invest inbusiness processes. The following describes how MTSs' represent productsand markets and how they supply the structure required to facilitate thestudents' performance of their required tasks.

Products:

Products in known MTSs generally include three types of product traits:business process traits, aggregate traits, and attributes. Businessprocess traits represent the outcome of business processes, such ascustomer service level and delivery delays. Aggregate traits describethe whole product, such as product quality and product reliability.Attributes represent specific features comprising a product. Attributescan vary quantitatively (for example, amount of calories in one servingof a breakfast cereal) or qualitatively (for example, a product'scolor). The values that attributes can express are calledcharacteristics. The set of characteristics that an attribute canexpress is referred to as the attribute's domain. The composite producedby the characteristics expressed by a product's attributes is called aproduct's design.

Product Classes:

A product class is the set of products consisting of all the possiblevalues for a product vector. Real world examples of product classes aresports cars and long distance phone service. A specific product isidentified by its class and its traits. For example, suppose sports carshave three traits: customer service, delivery delay, and productquality. Suppose also that customer service and product quality aremeasured with a ten point scale. A specific product in the sports carproduct class is a sports car with a level five customer service, twoweek delivery delay, and a level seven quality.

To provide more realistic decision situations, some MTSs furnish severalproduct classes, for example sports cars and luxury cars. Multipleproduct classes are defined by declaring their existence. For example,an MTS might declare three classes of products (classes A, B, C) bydeclaring three types of product vectors of the type described above.Each product class can have the same traits, but this is not necessary.

Markets:

Demand for products is simulated in prior art MTSs using a demandfunction for a market manipulator (FIG. 2, field 202). In most MTSs, themarket manipulator is a set of equations. For examples see: Steven Goldand Thomas Pray, “Modeling Demand in Computerized Business Simulations,”in Jim W. Gentry (ed.), Guide to Business Gaming and ExperientialLearning, Association for Business Simulation and Experiential Learning(East Brunswick: Nichols/GP Publishing, 1990), pp.117-138. The marketmanipulator takes the firms' production as input and calculates thetotal size of the market and the share of demand for each firm. Thisdemand is then compared to firms' actual production to determine sales.When equations are used, the parameters of the equations permit an MTSdesigner to adjust the industry and firm specific demand elasticitiesfor each product trait. In addition, by using multiple sets of theseequations MTSs can represent multiple market segments (for example,customers who value quality over timely delivery or vice versa) and/ormultiple markets (for example, the Canadian and the United Statesautomobile markets).

It is notable that, usually, the market manipulator does not directlyreceive product characteristics as inputs (as independent variables).Instead, a product's characteristics are used to produce a single numberthat represents a market's evaluation of a product's design. I call thisnumber a product's value. The conversion is produced by a productevaluator (FIG. 2, field 203). In most MTSs, the product evaluator is anequation v=h (a₁, a₂, . . . a_(n)), where v is the value of a product, nis the number of attributes comprising products, and a₁, a₂, . . .a_(n), are the attributes that can express characteristics in theproduct. I call this equation a product value function. The productvalue function has the effect of removing a product's attributes fromthe product vector and replacing them with a single aggregate producttrait: product value. The market manipulator accepts this trait as aninput. As described in detail in the closing remarks below prior artMTSs evaluate pro duct v alues using a distance value function.

Management Decisions:

Students are told what product classes, market segments, and marketsexist and the product traits comprising the products of an MTS. Withthis knowledge, students control a firm and compete in the simulatedmarketplaces by producing products from one or more of the declaredproduct classes.

Each student manages his firm by performing the following tasks:

1. A student studies the predefined markets and the behavior of theother firms (his competitors). From this analysis, the student developsa business strategy or adjusts his previous strategy.

2. The student enacts his strategy by selecting the characteristicsexpressed by product attributes, by setting prices, and by distributinghis firms' operating budget among business processes (for example,manufacturing, sales, advertising, and research and development). Theseinvestments are risky. If the strategy does not produce sufficientrevenues, the return on investment will be negative. The firm will losemoney and go bankrupt.

The tasks of market analysis, competitor analysis, and investment inbusiness processes are described below.

Market and Competitor Analysis:

Students analyze the marketplace through three methods:

1. Students analyze the marketplace results. They identify the prices,quantities, and product traits of products sold in the marketplace. Fromthis information they estimate the size of market segments and the valuethat customers gain from each product trait.

2. In some MTSs students can supplement the marketplace information bypurchasing computer generated marketing surveys. These surveys describethe characteristics of the simulated market (for example, demographicstatistics) or the results of simulated standard marketing tests (forexample, side-by-side product comparisons or focus group tests).

3. In some MTSs students can supplement the marketplace information bypurchasing marketing reports. Among other qualities, marketing reportsmight list products, prices, new products, products that sold well,products that sold poorly, and sales volume by product type.

Students analyze competitors using two methods:

1. By analyzing marketplace results, a student can learn the marketshare, production, prices, and products of competitors.

2. Some MTSs supplement this information with a computer generated‘competitive intelligence’ report that details competitors' behavior. Itmight state, for example, the average industry investment in productioncapacity or in research and development.

From a student's marketing and competitor analysis, he develops abusiness strategy. The business strategy states a focus on specificproduct classes, markets, and market segments. It states the desiredvalues of product traits, prices, and production volumes. A studentenacts his strategy with three decisions: set the attribute levels, setthe prices of its products, and invest in business processes. Thesedecisions are described below.

Setting Product Attributes and Price:

Students set the characteristics expressed by their products'attributes. In setting characteristics, a student determines a product'sdesign and is essentially designing a product in the simulation. Theonly restrictions on product design are the domains of the attributes.For example, quantitatively varying attributes might be bounded byminimum and maximum values. Likewise, qualitatively varying attributesmight present students with a limited number of characteristics tochoose from. Students also select their products' prices, subject torange limitations (for example, prices must be positive numbers).

Investing in Business Processes:

Students improve their product's business process traits and aggregatetraits by investing in their firm's business characteristics (forexample, purchasing/scraping production capacity, retooling a factory,hiring new salespeople, or purchasing more advertising). The results aredetermined by equations that take the firm's characteristics and thestudent's investment decisions as the independent variables and yieldthe values of business process traits.

Equations giving a firm's characteristics can affect either businessprocesses traits or firm characteristics, such as labor productivity.For examples of the use of equations in determining business processtraits and firm characteristics, see: Steven Gold and Thomas Pray, “TheProduction Frontier: Modeling Production in Computerized BusinessSimulations,” Simulation and Games, vol. 20 (September 1989): pp.300-318; Precha Thavikulwat, “Modeling the Human Component inComputer-Based Business Simulations,” Simulation and Gaming, vol. 22(September 1991): pp. 350-359; Steven Gold, “Modeling Short-Run Cost andProduction Functions in Computerized Business Simulations,” Simulationand Gaming, vol. 23 (December 1992): pp. 417-430; and PrechaThavikulwat, “Product Quality in Computerized Business Simulations,”Simulation and Gaming, vol. 23 (December 1992): pp. 431-441.

The closing remarks below provide a more detailed description of theprior art of MTS and also provides a general description of the priorart methods of modeling innovation, modeling technological advance, andthe prior art product value functions.

The prior art MTSs suffer from six primary deficiencies:

1. The prior method of modeling innovation only simulates the outcome ofinnovation (success or failure). It does not model the processes thatproduce the outcome. Because of this, prior art MTSs do not offerstudents the opportunity to experience the process of innovating or theopportunity to learn how to manage innovation.

2. Representing only the outcome of the innovation process, the priorart method of modeling innovation does not represent the role ofinformation, knowledge, and decision making in innovation. As a result,the prior art represents the management of innovation as an investmentdecision (how much to invest and when) rather than as a task ofproducing, exploiting, and managing knowledge.

3. The prior art method for simulating technological advance onlysimulates a small number of new opportunities. Real technologicaladvances create a multitude of opportunities. Because of thisdeficiency, prior art MTSs cannot provide students with practice inmanaging through technological change. Moreover, this deficiency willadversely affect an MTSs' dynamics and simulation of competitivemarkets.

4. Because of the value function used by prior art MTSs, prior art MTSare suitable only for teaching the management of established businesses(low uncertainty situations). These situations include, for example,pricing, designing, positioning, and promoting products in establishedmarkets (i.e., basic marketing). This limitation on their effective usearises from three consequences of the value functions that they use:

4.1. Students can choose any attribute, leave all other attributesunchanged, and increase a product's value by improving thecharacteristic expressed by the chosen attribute (assuming the chosenattribute is not already expressing its ideal characteristics). Becauseof this, a student can address each attribute independently.

4.2. By making a series of small changes in a product's design, astudent can produce a sequence of designs such that (1) each subsequentdesign increases product value and (2) the sequence ends with the idealproduct. Furthermore, this property holds regardless of the order inwhich a student addresses the product attributes.

4.3. The marketplace information produced by prior art MTSs is highlyreliable. Information about the value of products provides a lot ofinformation about the value of all other products.

 Because of these three qualities of prior art value functions, knownMTSs are not suitable for teaching the management of entrepreneurialenterprises (high uncertainty situations). These situations include, forexample, developing new core competencies, developing radicalinnovations, managing technological change, and reinventing one'sbusiness.

5. The prior art poorly models knowledge and knowledge concepts. Becauseof this, known MTSs cannot usefully address the role of knowledge in astudent's decisions or management of his simulated firm (such as,innovation, core competencies, and the management of risk). Neither canthe prior art represent the influence of knowledge on an industry'sdynamics.

6. Prior art MTSs cannot illuminate nor analyze a student's decisionprocedures—even though changing these procedures is their goal. Becauseof this, known MTSs must teach through an indirect method. With repeatedsimulations of a decision situation, a student tests a variety of ideasand analyzes the consequences. When the consequences differ from hisexpectations, he is surprised. Through iterative trial, analysis, andsurprise, he learns. With this indirect method, a student learns only aswell as he invents ideas and induces lessons.

The present invention improves over the prior art by creating a newmodeling relationship between a product's design and its value. Theconsequences of this change are great. The present invention provides asuperior model of innovation and technological advance, highlights therole of information and knowledge in management and in an industry'sdynamics, and provides a means of explicitly representing a student'sdevelopment and application of knowledge.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing brief description, as well as other features andadvantages of the present invention will be understood more completelyfrom the following detailed description of preferred embodiments, withreference being had to the accompanying drawings, in which:

FIG. 1 is a block diagram of an MTS.

FIG. 2 is a block diagram of the standard architecture of a competitiveindustry MTS;

FIG. 3 depicts a single peaked value function;

FIG. 4 depicts a multipeaked value function in a three-dimensionallandscape representation of product value verses two attributes;

FIG. 5 depicts a multipeaked value function in a matrix representation,with the matrix entries representing the value of the function fordiffering combinations of two attributes;

FIG. 6 depicts a ‘slice’ from FIG. 4 in a two-dimensional curverepresentation -wherein one attribute is held constant;

FIG. 7 depicts a ‘slice’ from FIG. 4 in a two-dimensional curverepresentation wherein one attribute is held constant;

FIG. 8 is a block diagram illustrating the architecture of an MTS inaccordance with the present invention;

FIG. 9 illustrates a product in the preferred embodiment;

FIG. 10 is a representation of a display presenting a firm'scharacteristics;

FIG. 11 is a representation of a display of a market database;

FIG. 12 is a representation of a display of an interface;

FIG. 13 depicts a student's portfolio of projects;

FIG. 14 depicts a measurement of a student's development of a corecompetency;

FIG. 15 is an illustration of a covariation contingency table;

FIG. 16 illustrates an object in the present invention;

FIG. 17 is a block diagram illustrating the architecture of acompetitive industry MTS in accordance with the present invention;

FIG. 18 is a block diagram illustrating the architecture of a ‘generalcase’ MTS in accordance with the present invention;

FIG. 19 is a block diagram illustrating the architecture of an auctionMTS in accordance with the present invention;

FIG. 20 illustrates a hardware arrangement for implementing the presentinvention;

FIG. 21 illustrates a process flow for evaluating a student's design;

FIG. 22 illustrates a process flow for developing the decision-makingskills of a user or for representing changes in design opportunities;and

FIG. 23 illustrates a form for providing search queries of themarketplace.

RELATED WORK

Gary J. Summers, “Modeling Innovation as a Process of Design inEducational Business Simulations,” in Developments in BusinessSimulation and Experimental Learning, vol. 26 (1999): pp. 146-152;

Gary J. Summers, “Analyzing Managers’ Judgments and Decisions with anEducational Business Simulation,,” in Developments in BusinessSimulation and Experimental Learning, vol. 26 (1999): pp. 58-64.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

The present invention realizes improvement to the marketplace model inMTSs by building upon new ideas from the fields of evolutionaryeconomics, evolutionary biology, and genetic algorithms, and fromstudies in the management of innovation, the present invention improvesupon MTSs. Primarily, and with additional important consequences, thepresent invention improves MTSs modeling of information, knowledge, andinnovation. For the purpose of exposition, the detailed description ofthe invention and preferred embodiment describe the specific class ofMTSs called competitive industry MTSs. Those versed in the art willappreciate that the invention described herein applies to MTSs thatemphasize other business situations. Thus, the later section titled“Other Applications” describes more general instantiations of thecurrent invention, and, in particular, the more general class of MTSsdepicted in FIG. 1.

An MTS in accordance with the present invention is a departure fromprior art simulations in its use of attribute-characteristicrepresentations of products, the inclusion of product categories, andthe use of new product value functions and correlations. These conceptsare introduced and defined below, and the consequences of incorporatingthese new features into MTSs is discussed thereafter in connection witha preferred embodiment of the invention.

Introduction and Definitions

Products:

Recall that some known MTSs describe products with attributes. Forexample, one may describe the design of an automobile with a list ofattributes that includes physical qualities (such as color, size, andshape), features (such as antilock brakes and power windows), andabilities (top speed, miles per gallon city, miles per gallon highway).Using this representation scheme, automobiles are objects composed ofthe following attributes (style, engine type, drive train type, exteriorcolor, window feature, brake feature, top speed, mpg city, mpg highway).Each attribute varies either quantitatively (for example, mph city andtop speed) or qualitatively (for example style). Using a value functionof a type described below, one can also have attributes that vary bothqualitatively and quantitatively. An example of such an attribute iscolor. Color may vary qualitatively (for example, red, blue, and green)and in intensity (light blue to dark blue). One can represent intensityquantitatively with a number (for example, a ten point scale). I call anattribute that can vary both qualitatively and quantitatively a dualvarying attribute. Including a dual variable attribute in the automobileexample, automobiles are objects composed of the following attributes(style, engine type, drive train type, exterior color (intensity),window feature, brake feature, top speed, mpg city, mpg highway). Aspecific automobile is identified by the vector of characteristics(sports car, 4-cylinder engine, front wheel drive, blue exterior(intensity=5), . . . , electric windows, anti-lock brakes, 115 mph, 23mpg city, 33 mpg highway). With this method, every product design isrepresented with a unique vector of characteristics. This method ofrepresenting a product's design is called an attribute-characteristicrepresentation.

The attribute-characteristic representation is much more general thandemonstrated by the preceding automobile example. The number ofattributes in a product design can vary throughout a learning sessionand from product-to-product (like the way in which word length varies ina game of scrabble). Attributes can be real valued (such as top speed),integer valued (such as an integer scale of one to ten), or qualitative(such as a letters). In the case of qualitatively varying attributes,each attribute can express characteristics from a different sets ofcharacteristics (colors verses styles in the automobile example);characteristics from the same set, with duplications allowed (forexample, letter combinations that produce words); or characteristicsfrom the same set, without duplications (permutations). In addition,attributes can be diploid (such as dual varying attributes and thedominant-recessive genes made famous by Gregor Mendal's experiments withpeas), triploid, or even more complex. Also, the attributecharacteristicmethod of accounting for a product's design can be recorded as a vector(as done above), with a matrix, as a single number, or through othersuitable means.

The attribute-characteristic representation provides the means forrepresenting all valid product designs. Recall that an attribute'sdomain specifies all of the possible characteristics that the attributecan express. The set of all valid products is produced by taking thecross-product of all the attribute domains (that is, taking everycombination of characteristics).

The form of the attribute-characteristic representation is determined ona case-by-case basis with regard to the purpose of the MTS, the valuefunction utilized (see below), and the available data structures.

Product Categories:

A product set defined by product characteristics is called a productcategory. A notable quality of the attribute-characteristicrepresentation of a product's design is that one can easily define setsof products by characteristics. This quality is not important in priorart MTSs but, for reasons described below, is important in the presentinvention. For qualitatively varying attributes, one defines a productcategory by the presence or absence of one or more characteristics.Three examples of product categories are (1) sports cars, (2) cars withfour cylinder engines, and (3) sports cars with four cylinder engines.On the other hand, for quantitatively varying attributes, one defines aproduct category by specifying a range of values for the attribute.Three examples of such product category are (1) cars with a top speedbetween 90 mph and 110 mph, (2) cars that have at least 20 mpg city, and(3) cars with a top speed between 90 mph and 110 mph and have at least20 mpg city. In defining product categories, dual varying attributes arespecified by the combining the two methods illustrated above (forexample, blue cars with color intensity between 3 and 7). A productcategory, therefore, can be defined based on the presence and/or theabsence of attributes and can include any combination of qualitative orquantitative or other type of attributes.

The Product Value Function:

The product value function explores the relationship and interactionamong the attribute-characteristics of a product design by quantifyingthe degree to which interactions among product attributes andcharacteristics affect products' values. Interactions exist when anattribute contribution to the value function depends upon thecharacteristics expressed by one or more other attributes. For example,how much value does a red exterior add to the value of a particularautomobile? This question is difficult to answer. The value of a redexterior depends upon an automobile's style. It is highly valued onsports cars but not on limousines. In this example, thecontribution-to-product value of a particular characteristic express byone attribute (here, exterior color) depends upon the characteristicsexpressed by other attributes, e.g., style). This effect is called aninteraction and for some attribute-characteristics can be associatedwith “frustration”.

Frustration occurs when improving one attribute's contribution toproduct value decreases the contributions of other attributes. Strongfrustration exists when the effect decreases the total product value andsuch frustration makes product design a difficult task. In theautomobile example, changing the characteristic expressed by the styleattribute from sports car to limousine increases the contribution of thestyle attribute to the product's value. Simultaneously, this changedecreases the contribution of the red color expressed by the exteriorcolor attribute. In total, the value of the automobile decreases.

As used herein, a product value function in which interactions producestrong frustration and therefore exhibit multiple optima are referred toas “multipeaked value function” (see the glossary). The presentinvention uses multipeaked value functions in an MTS to more closelymodel, among other things, innovation. These functions can be found inoptimization problems from a variety of fields, including combinatorialoptimization, genetic algorithms, cellular automata, computer science,molecular biology, management science, and evolutionary biology.Specific examples of optimization problems that include a multipeakedvalue functions include designing the layout of an integrated circuit,finding the shortest tour connecting a set of cities, schedulingproduction in a factory, and finding a protein that catalyzes aparticular reaction.

A Visual Metaphor:

One can understand the multipeaked value function, and its differencefrom the prior art distance value functions described in the closingremarks below using a visual metaphor. Consider all possible productdesigns to lie along a horizontal surface, with similar products lyingclose to each other and dissimilar products lying far from otherproducts. Assuming no dual varying or similarly complex attributes, if aproduct has n attributes, one would need an n-dimensional space toproperly accomplish this task. A visual metaphor is appropriate forconsidering a two attribute product.

To complete the visual metaphor, a mark is placed above each product ata height equal to the overall value of the product that it residesabove. When this task is completed, one has created a distribution ofproduct values over the surface of the products. This multidimensionalarrangement of products together with the distribution of product valuesis called a product space. The shape of the distribution of productvalues in a product space is called the product space's topology.

The product space's topology shows how design quality varies over theproduct space. The product space can be visualized as a mountain rangewith the most valued products lying on the surface beneath mountainpeaks and the least valued product lying on the surface beneath valleys.The topology of the mountain range significantly affects the nature ofstudents' task in an MTS and the dynamics of an MTS. Does the productspace topology resemble Mount Fuji: a single peak, with broad, smoothslopes? Does the product space topology resemble the Rocky Mountains:many peaks of varying heights, valleys of varying depths, and suddenchanges in altitude?

In both the visual metaphor and the real multidimensional product space,the amount of frustration among attributes determines the topology ofthe product space. If there is no strong frustration (as in the priorart, where there is no frustration—see the closing remarks), thetopology is Mount Fuji-like, that is, single peaked. FIG. 3 provides atwo attribute example. In this figure, products have two attributes(plotted along two of the axes), each of which expresses a real numberbetween one and four. The value function is single peaked like a pyramidor cone to pick two basic geometric forms. The product value varies fromzero to three (along the third axis).

However, when strong frustration exists, the product space can have amultipeaked topology. In formal terms, a multipeaked value function hasas least one local optimum, in addition to a global optimum. FIG. 4provides an illustration of a two attribute, multipeaked value functionin which the two attributes exhibit strong frustration. As describedbelow, it is this complex topology that produces the unique qualities ofthe present invention. For a good discussion of the differences betweensmooth and rugged value functions, see: Stuart Kauffman, The Origins ofOrder: Self-Organization and Selection in Evolution (New York: OxfordUniversity Press, 1993), chapter 2.

In addition to its multiple optima, multipeaked value functions of thetype used in the present invention possesses another property absentfrom smooth and some rugged value functions: an optimal product cannotbe discovered by varying the attribute-characteristics independently. Toillustrate this property, consider a product that has two attributes: a₁and a₂, where each attribute expresses an integer between one and ten.Suppose that the product in the product space with the highest value hasa₁=3. In the prior art value functions, for any value of a₂ three is thebest choice for a₁. Because of this, student's using prior art MTSs canfind the best product by treating each attribute independently (see theclosing remarks). In the example above, once a student has discoveredthat three is the best value for attribute one, he need not considerattribute one again. He can focus exclusively on finding the best valuefor attribute two. However, when a multipeaked value function is used,the best value for an attribute depends upon the characteristicsexpressed by other attributes. FIG. 5 demonstrates this quality.

FIG. 5 shows a value function for products that have two attributes,each expressing a characteristic from the set {A,B,C,D}. In FIG. 5, notwo rows have their highest value in the same column. Likewise, no twocolumns have their highest value in the same row. FIGS. 6 and 7illustrate this same quality for the value function depicted in FIG. 4.Each figure depicts a ‘slice’ that shows how the value function varieswith x₁ for a different particular value of x₂. As can be seen, the bestvalue for x₁ in FIG. 6 is not the best value for x₁ in FIG. 7. Becauseof this quality of multipeaked value functions, students cannot find theoptimal product by considering each attribute independently. Instead,students must simultaneously consider several attributes, and thisinteraction of attributes has consequences described below.

Correlation:

Correlation is a function of the product value function and is closelyrelated to product space topology. In MTSs in accordance with theinvention, students will use their knowledge of the value of one productdesign to predict the values of others and the quality (fit) of theirprediction depends on correlation. The capability of information aboutthe values of products to predict the values of other products is givenby measures of correlation. For this reason, measures of correlation areuseful in selecting the value function that produces the informationproperties desired for a particular MTS.

The predictive capacity of information increases and decreases withmeasures of correlation. These measures show that single peaked valuefunctions are highly correlated over the product space. Informationabout the values of products provides significant information about thevalues of many other products. For multipeaked value functions,correlation decreases with strong frustration. Specifically, if changesin product design or product category definition include changes toattributes that exhibit strong frustration, correlation will decreasefaster than in single peaked value functions. For an example of thiseffect, see: Bernard Manderick, Mark de Weger, and Piet Spiessens, “TheGenetic Algorithm and the Structure of the Fitness Landscape,” inProceedings of the Fourth International Conference on GeneticAlgorithms, edited by Richard Belew and Lashon Booker (San Mateo,Calif.: Morgan Kauffmann Publishers, 1991), pp. 143-150. As more orlarger such changes occur, correlation will decrease even faster.Because of this quality, correlation on multipeaked landscapes istypically high for only small changes. Metaphorically, this meansinformation is useful only in making localized predictions about themountain range topology.

Students can use either of two methods to predict product values:

1. Students will use their knowledge of the value of a specific productto predict the resulting value of design changes. For example, how welldoes the value of the automobile (sports car, 4-cylinder engine, frontwheel drive, blue exterior, . . . , electric windows, anti-lock brakes)predict the value of the automobile (utility vehicle, 4-cylinder engine,front wheel drive, blue exterior, . . . , electric windows, anti-lockbrakes)? The capability of knowledge of the value of one product topredict the values of other products depends on how correlation varieswith changes in a product's design.

2. Students may also use the value of products in a category to predictthe values of products in another category. For example, what do strongsales of front wheel drive sports cars indicate about the values of rearwheel drive sports cars? The values of products in one category will begood predictors of the value of products in another category if thecategories are correlated.

For each type of prediction, and for the purpose of selecting a valuefunction for use in an MTS, appropriate measures of correlation exist:

To calculate the predictive capacity of knowledge of the value of agiven product for the purposes of a design change, one uses anautocorrelation formula with changes stemming from the existing productdesign. For a description of the autocorrelation function, seeManderick, et al., supra. The autocorrelation function is usuallydiscussed with reference to qualitatively varying attributes. Forquantitatively varying attributes, one generates the required sequencesof designs by iterative applications of the following steps:

1. randomly choose an attribute,

2. randomly choose a number from the set {−x, +x}, where x is smallcompared to the range which the attribute can vary, and

3. add the chosen number to the attribute's value. By using a small x,one ensures that the perturbation of the attribute is a small step inthe ‘mountain range.’

To measure the correlation between two sets of products, one performsthe following procedure:

1. Form a set of products consisting of the union of the two productcategories.

2. From the union, randomly choose several different products (thequality of the estimate increases with the number of samples).

3. Ascertain that each randomly chosen product is a member of at leastone of the product categories. To calculate correlation, each of therandomly chosen products must be paired with a product from the otherproduct category. Specifically, pair the randomly chosen product withthe product in the other category that is most similar. If severalproducts tie on this criterion, randomly select one of these productsfor pairing. In the case of qualitatively varying attributes, similarityis defined as having the greatest number of characteristics in common.In the case of quantitatively varying attributes, similarity is measuredby a distance function, where products that are closer together are moresimilar.

4. For each pair of products, calculate the products' values.

5. From the pairs of product values, one can calculate the correlationbetween the two product categories using the standard equation fromstatistics.

To aid an MTS designer in selecting a value function, there are severaluseful correlation functions discussed in related academic research, aswell as their relationship to product space topology, see: Manderick etal., supra; see also Marc Lipsitch, “Adaptation on Rugged LandscapesGenerated by Iterated Local Interactions of Neighboring Genes, “inProceedings of the Fourth International Conference on GeneticAlgorithms, edited by Richard Belew and Lashon Booker (San Mateo,Calif.: Morgan Kauffmann Publishers, 1991), pp. 128-135; StuartKauffman, The Origins of Order. Self-Organization and Selection inEvolution (New York: Oxford University Press, 1993), pp. 63-66. BernardManderick, “Correlation Analysis,” in Thomas Back, David Fogel, andZbignies Michalewicz, Handbook of Evolutionary Computation (New York:Oxford University Press, 1997), section B2.7.3 (hereafter referred to asHEC).

Some Examples of Value Functions:

The attribute-characteristic representation and the value function worktogether; however, in order to apply an attribute-characteristicrepresentation, one must define a value function that can accept itsform (that is its combination of qualitative, quantitative, and other,more complex attributes) or else the attribute-characteristicrepresentation cannot be used in a model or simulation. “ManagementApplications and Other Classical Optimization Problems,” by VolkerNissen in section F1.2 of HEC provides list of references of academicarticles that investigate the optimization of these kind of valuefunctions. From reviewing these articles, one can find many examples ofsuitable value functions, some of which may be found to be suitable tothe task upon experimentation. Some examples of suitable multipeakedvalue functions for modeling innovation include:

Quantitatively Varying Attributes:

The “after Fletcher and Powell” function described in Thomas Back'sEvolutionary Algorithms in Theory and Practice (New York: OxfordUniversity Press, 1996), offers a function for representingquantitatively varying attributes. In this case, each product attributerepresents a coordinate axis. Using Back's formulas, one may incorporateany number of attributes. Such a function can be applied here toaccommodate qualitatively varying attributes and dual varying attributesby coding one or more of the coordinate axes into a discreterepresentation. Genetic algorithms frequently convert a real numberedaxis into a base two (bit string) representation. A description of thisprocess is given in Alden Wright's, “Genetic Algorithms for RealParameter Optimization,” in Foundations of Genetic Algorithms, edited byGregory Rawlins (San Mateo, Calif.: Morgan Kauffman Publishers). Usingan analogous process, one can convert a real numbered axis into adiscrete representation of any base and with any number of attributes.

Qualitatively Varying Attributes:

1. One can use any continuous multipeaked function to modelqualitatively varying attributes by converting the axes into a discreterepresentation. For example, one can convert each axis of a twodimensional “function after Fletcher and Powell” function intohexadecimal representation that has six digits. This would produce aproduct that has twelve qualitatively varying attributes, eachexpressing sixteen characteristics.

Nk-landscapes:

Biologist Stuart Kauffman developed nk-landscapes in his research intoto the properties of rugged value functions. For a description ofnk-landscapes, see Stuart Kauffman's The Origins of Order.Self-Organization and Selection in Evolution (New: York OxfordUniversity Press, 1993), chapter 2. The nk-landscape is particularlyuseful because its parameters permit one to easily adjust itscorrelation properties.

2. Many combinatorial optimization problems provide suitable valuefunctions. For example, one can look to scheduling or packing problemsfor suitable functions. Ralf Bum's article “Scheduling,” in section F1.5 of HEC describes scheduling problems and also describes, withreferences, several alternative attribute-characteristic representationsthat one may use with scheduling problems. Similarly, Kate Juliff'sarticle, “The Packing Problem,” in section F1.7 of HEC describes packingproblems and also describes, with references, several alternativeattribute-characteristic representations that one may use with packingproblems.

Dual Varying Attributes:

The product value function for representing dual varying attributes canbe the objective function used in combinatorial optimization problem ofa traveling salesman problem (TSP). “The traveling salesman problem isthe problem of visiting each vertex (i.e., city) in a full connectedgraph exactly once while minimizing a cost function defined with respectto the edges between adjacent vertices. In simple terms, the problem isto minimize the total distance traveled while visiting all the cities[in a set of cities] and returning to the point of origin.” DarrellWhitley, “Permutations,” in section C1.4 of HEC, p. C1.4:1. When usingthe objective function of a TSP, the characteristics expressed byattributes are the destinations (cities) in the TSP.

In order to create dual varying attributes, in accordance with an aspectof the invention, one adds a reference point to the TSP. The intensityof any attribute is equal to the distance form the reference point. Asstudents adjust the intensity of a dual varying attribute, thedestination expressed by that attribute moves so that the new intensityequals the distance between the destination and the reference point. Aproduct's value is then calculated with this new configuration ofdestinations. Note that with this method, any number of attributes canbe converted from qualitatively varying attributes to dual attributes.

What Kind of Products Does the New Method Use?

What kind of products could the system just described represent?Utilizing this system for ‘real’ products is problematic; one will havegreat difficulty in matching the multipeaked value function to a realproduct. Two methods can resolve this dilemma. First, the product couldbe the subject of an optimization problem. For example, if the objectivefunction from a scheduling problem is used as the multipeaked valuefunction, the products can be schedules. Second, the products could beabstract. For example, if an nk-landscape is used as the multipeakedvalue function products can be strings of letters, as in the preferredembodiment. Similarly, if a function after Fletcher and Powell is usedas the multipeaked value function, then the products can be real valuevectors. Because students will have difficulty ‘feeling’ that they aremanaging a business when the product is abstract, one can give abstractproducts a visual representation, such as plants or flowers.

A Preferred Embodiment of The Invention

A basic MTS embodying the present invention is described with referenceto FIG. 8 to focus attention on the construction and workings of themodel of the invention in an MTS. In the following discussion, productsare described as including only one trait. However, they can includetraditional conventional further traits inlcuding business processtraits and aggregates. Thus, in the following description, products haveonly qualitatively varying attributes. In addition, for simplicity, theinterface is minimal, and firms have few characteristics. Thoughunembellished, the embodiment shows how to incorporate multipeaked valuefunctions in an MTS. From this example one can construct moresophisticated MTSs, including product definitions which includesystem-set attributes.

FIG. 8 displays the architecture of an MTS in accordance with theinvention. Many of the components are similar to those a conventionalMTS: a marketplace (801), a plurality of firms controlled by students(805), and interfaces (807). In addition, however, an MTS in accordancewith the invention includes a market database (806) which containsrecords of each product's sale in all rounds of a learning session. Withthis additional information, students can analyze the entire history ofthe marketplace.

Preferrably the MTS comprises of two programs and a spreadsheet file:

1. Program #1 models the (a) marketplace (801) and (b) firms (805) and(c) provides an interface (807) for each student.

2. Program #2 is a spreadsheet program for viewing and analyzing themarketplace results (for example, Microsoft Excel or Lotus 123).

3. A spreadsheet file defines the market database (806) containing arecord of all of the marketplace results.

The functionality and operation of these components are discussed next.

1(a): Marketplace Specifications

Products:

Products are comprised of an arbitrary number of attributes (e.g.,n=10). Each attribute varies qualitatively and can express one oftwenty-six characteristics. These characteristics are represented by theletters of the alphabet. For example, the sequences ‘ASDFGHJKLL’ and‘QWERTYUIOP’ are different products. FIG. 9 displays the product‘ASDFGHJKLL’ having ten fields (901-910). The letter in each field isthe characteristic expressed by (that is, the instantiated value of) thecorresponding attribute in the product ‘ASDFGHJKLL’.

The Product Value Function:

In a preferred embodiment, the value of a product is calculated with annk-landscape function, although other multipeaked value functions can beused. An nk-landscape has four parameters that are important for MTSs.These are (1) the number of product attributes, n, (2) the number ofcharacteristics that each attribute can express, b, (3) the averagenumber of interactions per attribute, k, and (4) the arrangement of theinteractions over attributes. The value of k is particularly important.It permits adjusting the amount of interactions, frustration, andcorrelation in the nk-landscape. By adjusting k one can achieve anappropriate multipeaked value function for use in the present invention,as described next.

The values of n, k, b are selected to produce an appropriate productvalue function. As the number of product attributes n increases, thevariation in product values decreases. For this reason, products in theMTS preferably have fewer attributes and a greater number ofcharacteristics. This allows for sufficient variation in product valueswhile still presenting students with a sufficiently difficult designproblem.

In the embodiment described herein, n=10, b=26, and k should have avalue of 2≦k≦4. One can arrange interactions evenly over attributes.This produces a value function with high correlation for small changesin product design (for example, a change of a single characteristic) andlow correlation for more substantial design changes. From theserecommendations, one of skill may adjust the values of the n, k, and bparameters to suit their particular MTS needs.

The Market:

In the marketplace model of this illustrated embodiment, products haveonly one trait: product value. A market manipulator 802 accepts productvalue as its independent variable and calculates demand each round. Tocalculate demand, the market manipulator preferably uses a set ofequations as described in U.S. Provisional Application Ser. No.60/094,900, filed Jul. 31, 1998, or the Gold and Pray system of demandequations. Steven Gold and Thomas Pray, “Simulating Market-andFirm-Level Demand Functions in Computerized Business Simulations,”Simulations and Games, vol. 15 (September 1984): pp. 346-363.

Technological Advance:

At the start of a learning session, the MTS restricts the domains of oneor more attributes, thereby limiting product design to sufficiently lowvalued products (for example, only characteristics ‘A’ through ‘G’ areallowed in product designs). Students compete by searching for the bestset of characteristics to define a product. During the learning session,the restrictions are relaxed, either incrementally (a fewcharacteristics each round) or altogether (all restrictions removed in asingle round), as described below.

Royalties:

Define products as similar if they differ by less than a predeterminednumber of characteristics. A product is new to the marketplace if (1) itis appearing in the marketplace for the first time and (2) no similarproducts have appeared in the marketplace. If a firm produces a productthat is new to the marketplace it has rights to the product and allsimilar products. This means that, for a limited number of rounds, ifcompeting firms produce the product or a similar product, they must paya royalty to the inventing firm. The duration and size of the royaltyare adjustable parameters, set at the start of the learning session. Oneversed in the art can easily set the royalty parameters as desiredand/or to fit real-world industry practice.

Manufacturing:

Each unit of production capacity is best suited for manufacturing aparticular type of product. This is called its specificity. Productioncapacity's specificity is designated by a product design. For example,capacity of type ASDFGHJKLL is best suited for producing the productdesign ASDFGHJKLL. Producing any other type of product increases thevariable cost of production. For example, let Z represent the numbercharacteristics in which the design of a product to be manufactureddiffers from the specificity of the production capacity used in theproduction. Let Y be the base unit variable cost, and let d be aconstant. The cost for producing each unit of product is:

Unit variable production cost=(d*Z)+Y

The value of d is set by the MTS at the start of the learning session.

Production capacity is purchased/scraped in blocks of capacity (forexample, one hundred units). The cost of a block is constant over allspecificities and throughout the learning session. The same is true ofthe scraping value. Similarly, the base variable production cost isconstant over all product designs and throughout the learning session.One versed in the art can easily set the manufacturing parameters asdesired and/or in accordance with real-world examples.

1(b): Firms' Specification

In this MTS, all firms are controlled by students. As shown in FIG. 10,firms have two characteristics: (1) a budget 1001 and (2) productioncapacity 1002 of identified specificity and unit capacities. The firm ofFIG. 10 has a budget of five hundred and ten dollars and two types ofproduction capacity. The firm has seventy-five units of capacity ofspecificity QWERTYUIOP and twenty-five units of specificity ASDFGHJKLL.With this capability, this firm can produce one hundred units ofproducts each round (assuming its budget covers the variable costs ofproduction and royalties).

In addition to production capacity, firms have three routines ormethods. (Firms are programmed as objects in an object orientedprogramming language). These routines (1) update the firms' budget, (2)update the firms' product capacity, and (3) send the firms' products tothe marketplace.

1(c): The Interface

Each student has one interface. The interface has four fields forrecording a student's decision. It also has routines for sendinginformation to a student's firm (e.g. by posting the results from aform). (The interface is programmed as an object in an object orientedprogramming language).

2: Students' Tasks

Marketing Analysis:

Each round, the marketplace results are recorded in the market database.Using charts, graphs, and/or any means that they deem appropriate, eachstudent analyzes the market database.

Management:

Based on his analysis of the market database, each student (1) analyzesthe marketplace result, (2) designs products, (3) chooses products tomanufacture, (4) buys and sells production capacity, and (5) determinesproduction schedules.

The interface helps a student keep track of his decisions. FIG. 12depicts an interface. The interface contains four fields:

1. Field 1201 shows the student's firm's budget.

2. Field 1202 shows a student's firm's production capacity. The leftside lists the specificity of the production capacity. The right sidelists the units of capacity. FIG. 12 depicts two types of capacity:QWERTYUIOP with seventy-five units and ASDFGHJKLL with twentyfive units(“Units”).

A student purchases/sells production capacity by increasing/decreasingthe maximum production listed in the right column. The student can alsopurchase new production capacity by adding a new row to the list. Thismethod must be used when purchasing production capacity with aspecificity that differs from the firm's current production capacity. Asa student purchases/sells production capacity, the interfaceautomatically adjusts his firm's budget (displayed in field 1201).

3. In field 1203 a student enters his firm's production schedule for thecurrent round. In its three columns, the student lists the products tobe produced, the production amounts, and the capacity utilized. FIG. 12shows five entries. Notice that separate entities are required wheneverthe product or utilized capacity differs. As a student develops aproduction schedule, the interface automatically adjusts his firm'sbudget (displayed in field 1201).

4. Field 1204 is the ‘manufacture and ship button.’ When satisfied withhis production decisions, a student uses a mouse to ‘click’ on thisbutton. This signals that his decisions are complete. If the productiondecisions have not reduced the budget to negative values, the interfacesends the student's production decisions to his firm.

A negative budget means that the student's production decisions requiremore capital than the student's firm has in the current round. If astudent tries to ‘manufacture and ship’ with a negative budget asdetermined by a script, function call, or applet; in a conventionalmanner, the interface alerts the student to the problem. The student canthen adjust his capacity and production schedule accordingly.

3: The Market Database Specifications

The market database is a spreadsheet file on a student's computer. FIG.11 shows a market database. Firms' production are listed in rows, witheach row listing a specific type of product produced by a firm. Thefirst column of this file lists the period that products were sent tothe marketplace. The next ten columns specify the product type bylisting its characteristics. Column twelve lists the firm that producedthe products. Column thirteen lists the number of products sold in themarketplace.

4. The Operation of the Preferred Embodiment of an MTS

The MTS of the invention generally progresses through the five steps ofprior art MTSs which are repeated each round. In addition, the presentMTS also requires two additional steps that occur only once during alearning session. First, the MTS initializes the learning session beforethe initial round. Second, the MTS simulates a technologicalbreakthrough during the learning session. Below, I describe the fivesteps repeated each round and then I describe the two additional steps.

The Five Steps Comprising Each Round

Steps One, Two, and Three:

As described earlier, the first three steps of an MTS consist of eachstudent (1) analyzing the marketplace information, (2) making decisionsfor his firm, and (3) sending these decisions to his firm through theinterface. This includes the following tasks:

1. Each student views and analyzes the market database for the purposeof designing products and setting a production schedule for the round.To accomplish these tasks, students utilize charting, graphing,intuitive heuristics, and/or other means that they deem useful.Necessarily, as described below, students hypothesize product categoriesand perspectives. For simplicity, this embodiment does not record oranalyze this process.

2. Based upon their analysis of the market database, each studentdesigns new products for his firm.

3. Choosing from the products previously offered to the marketplace andfrom his new designs, each student selects products to manufacture inthe current round.

4. Each student determines the production volume for each product thathe will manufacture. If desired, each student can buy new productioncapacity or sell unused production capacity. When making productiondecisions, a student cannot exceed his firm's budget. Students shouldaccount for the cost/revenue of buying/selling production capacity, thevariable production costs, and royalties.

5. Using the interface, each student sends his production plans to hisfirm.

Step Four:

After the interface sends a student's production plans to his firm, theMTS causes for each firm (1) an update to its production capacity, (2)an update its budget, and (3) sends the products and production volumesof that firm to the marketplace.

Step Five:

The marketplace receives the production from firms. Using annk-landscape function as a product value function described above, aproduct evaluator (FIG. 8, field 803) evaluates each product. Afterproducts are valued, the market manipulator (FIG. 8, field 802) takesthe products' values as input and calculates demand using either theequations in the aforesaid provisional patent application or a Gold andPray system of demand equations. From the demand and firms' production,the marketplace calculates sales. The marketplace then records theresults in the market database and sends the revenues to the appropriatefirms. Subsequently, the firms' routines update their budgetsaccordingly.

After completing these five steps, the round is complete, and the nextround, if any, begins with step one.

Additional Steps for Initializing the Learning Session

Initializing:

Before a learning session can begin, the computer must initialize thelearning session. To accomplish this, it performs the following steps:

1. The computer gives each firm a starting budget.

2. The computer simulates a round of sales and places the results in themarket database. This is done so that the market database will containanalyzable data for the first round of a learning session (data as usedherein includes the singular). To simulate sales, the computer randomlygenerates a sufficient number of products and ‘manufactures’ apredetermined number of each product. The computer then calculates salesusing the procedure presented above The results are placed in the marketdatabase, listing the round as zero. No revenues are sent to firms.

3. So that the MTS can simulate an industry life cycle in later rounds,the design restrainer (FIG. 8, field 804) restricts the domains of oneor more product attributes so that students can only design products ofsufficiently low value. To do this, the computer searches randomly (orwith an algorithm such as a genetic algorithm) for a product ofsufficiently low value. Once one is found, the computer identifies acorrelated set of products. The computer does this by identifying theattributes that affect the greatest number of other attributes (the mostinteractive attributes). The domain of each of these attributes isrestricted to one characteristic: the characteristic that it expressesin the identified low value product. At the start of the learningsession, only the products that conform to these constrained domains arevalid products.

Simulating a Technological Breakthrough:

Through competition, students will settle on a category of products fromthe initially valid set of products. Once this occurs the number ofinnovations in each round will decrease. The decrease occurs because asdesigns improve it becomes more difficult, and therefore costly, forstudents to find better designs from the same product category. The MTSmonitors the rate of innovation. When the rate of innovation issufficiently low, the design restrainer, 804 of FIG. 8, as implementedby the central computer, 2010 of FIG. 20, or instructor, 2020 of FIG.20, expands the domains of the product attributes that have beenrestricted. This simulates a technological breakthrough. The designrestrainer can restore the full domain of the attributes in a singleround or does so piecemeal over several rounds. As domains expand,students can search through the larger set of allowable products. Whenall of the restrictions are removed, students can search the entireproduct space.

Use of the MTS of the Preferred Embodiment

The MTSs' of the preferred embodiment models changes the simulation ofinnovation and technological advance. It also fundamentally affects thestudents' tasks of analyzing marketplace results and designing products.As these tasks are central in any MTS, all other tasks that an MTSdemands of students are also affected, as well as the dynamics of thesimulated industry. The use of an MTS according to the invention and itsimpact on the learning process are described below.

Designing Products:

Students design products by selecting the characteristic expressed byeach product attribute. When designing products, students face twoproblems. First, there are an enormous number of designs. In thedescribed embodiment, for example, products can have ten attributes witheach attribute expressing one of twenty-six characteristics. Thus,students can choose from 26¹⁰ unique products. A student can consideronly a small number of these possibilities. Second, attributes interactand produce strong frustration. Because of this, students cannotoptimize design by considering each attribute independently. Instead,each student must discover valued combinations of characteristics.

A student efficiently designs high value products by hypothesizing andevaluating product categories. By using product categories, a studentgreatly simplifies the design problem and learns decision making skillsin the process. He can evaluate the potential of an entire category ofproducts rather than evaluate every single product. Specifically, astudent evaluates a category by observing the marketplace performance ofa few products from that category. If the product category showspotential (its products fair well in the marketplace competition), thestudent concentrates his effort and investment in that category. If thecategory evaluates poorly, the student hypothesizes new categories thathe believes will produce better results and implements those reviseddesigns in subsequent rounds.

Projects:

In order to develop valuable products more quickly and efficiently, thestudent hypothesizes several product categories and searches within eachone. The exploration of each hypothesized product category is called aproject. In the MTS, a student will manage a portfolio of projects,deciding when to initiate new projects, when to cancel projects, and howto distribute his firm's budget among projects. Selecting productcategories to search is an important decision. The product categoriesthat a student focuses upon define his business.

FIG. 13 depicts a firm's portfolio with projects defined uponqualitatively varying attributes; however, the portfolio can containprojects defined upon quantitatively varying attributes or other typesof attributes. In this figure, product categories are defined by listingthe characteristics that define a category and placing a number sign inthe remaining attributes. The number sign indicates that theseattributes are not part of the category definition. For example,(ABC#######) represents the product category where the letters A, B, andC are expressed in the first, second, and third attributes,respectively. The products (ABCYHUKMNR) and (ABCRDWSZGY) are members ofthis category.

FIG. 13 shows four projects. Projects (#SD#G###LL) and (QWE####IOP) arecash cows. They produce products that are successful in the marketplace.The student managing this firm exploits these product categories throughproduction; they provide his firm's revenues. Although the student hastwo cash cows, competition compels him to search for higher valueproducts. He must find higher value products more quickly andefficiently than his competitors or suffer a competitive disadvantage.Product categories (#####H#BNT) and (XYZ#######) are the student'shypotheses of product categories containing higher value products.Production from these projects will likely be small as the studentfocuses on evaluating these categories.

Perspective:

Students do not randomly hypothesize product categories or randomlydesign products. Instead, they hypothesize product categories and designproducts after studying the marketplace results, for example, as may beprovided in the marketplace database (see FIG. 11). In studying themarketplace results, students try to identify characteristics thatcontribute significantly to products' values. These characteristics canbe identified by their appearance in products that are successful in themarketplace and their absence from products that are not successful inthe marketplace. If a student desires information not provided by theprevious marketplace results, he will experiment by manufacturing asmall quantity of products and offering them to the marketplace. Havingidentified valued characteristics, a student will combine thesecharacteristics to create products.

A student faces a difficult problem in analyzing marketplaceinformation. The marketplace produces an enormous amount of informationwhether attributes vary qualitatively, quantitatively, or both.

To cope with the voluminous information, a student must select theinformation that is most effective and relevant to his business. Heaccomplishes this by evaluating only a few product categories. Thesecategories might include, for example, the student's projects, potentialprojects, and product categories defining his competitors' products (asdefined by the student). I call this set of product categories astudent's perspective.

A perspective has the effect of categorizing the marketplace data. Indoing so, it filters the market information, selecting the informationthat a student feels is most important. It is the means through which astudent ‘frames’ the complex problem of competing, surviving, andprofiting. It can be interpreted as a student's definition of themarket. Different perspectives filter the marketplace resultsdifferently. Students with different perspectives will identify and missdifferent opportunities; evaluate product categories differently; andvalue information differently. Results that are surprising to onestudent might easily be anticipated by a student with a differentperspective.

Innovation:

Because students design products, innovation is defined in terms ofproduct design. Specifically, an innovation is a product that differsfrom the previous products offered to the marketplace by at least onecharacteristic. A student's innovatins will come from the productcategories that he searches. Every product category possesses a uniquedistribution of product values. Because a student determines the productcategories that he searches, he determines the distribution of productvalues corresponding to the innovations that he may produce. The studentdetermines whether he searches a barren category or one pregnant withinnovations. Moreover, a student changes the product categories that hesearches as he gains knowledge. As a result, innovation is primarily afunction of a student's development and application of knowledge—i.e.,knowledge management. In stark contrast, innovation in prior art MTSshave relied upon exogenously determined sets of new product designscoupled with innovation probabilities and are essentially an investmentdecision where spending more increases the probability of designing abetter products.

Short- and Long-Run Strategies:

Innovating via perspectives and product categories creates a dilemma forstudents. A student can direct his efforts and investment towardsproduct categories that the marketplace results have identified as mostpromising (categories that have done well in previous rounds). Byexploiting this ‘current’ knowledge, a student immediately increases hisfirm's profits and the competitive pressure on his competitors. This isa short-run strategy. Alternatively, a student can take a long-runstrategy and invest in discovering new product categories that containhigher value products (that is, new core competencies as describedbelow). By developing new knowledge, a student can gain a largecompetitive advantage in future rounds. This requires time andinvestment, and there is a risk that no such product categories will befound. Balancing investment between these two alternatives is thequintessential knowledge management dilemma.

A Continuum of Innovations, Incremental through Radical:

Using either the autocorrelation function with a specified product as astarting point, or by measuring the correlation between productcategories, the present invention permits defining a measurablecontinuum of innovation types. Consider the product categoriescontaining a significant number of the products offered to themarketplace in previous rounds. Incremental innovations are innovationsfrom product categories that are highly correlated with at least one ofthese product categories. Radical innovations are innovations containedin product categories that are not correlated with these productcategories. Innovation type is measured by these correlations.

Because the innovation measure can be defined as either changes in aproduct's design or as a comparison of product categories, this measureis relevant to students when they design products and study themarketplace results. If a new product is an incremental innovation,analysis of previous marketplace results provides a good prediction ofthe new product's value. Incremental innovation can rely primarily uponmarket analysis. In contrast, previous marketplace results are poorpredictors of the value of radical innovations. Because of this,inventing a radical innovation requires testing new products in themarketplace. Compared to incremental innovations, they require greatertime and investment. Their development also carries a greater risk offailure. With little guidance from previous marketplace results,students may not find any successful radical innovations.

Incremental and radical innovations have an obvious relationship to theproblem of exploiting knowledge versus developing new knowledge with theshort-run vs. long-run strategies. Investing heavily in incrementalinnovation is the exploitation of current knowledge. It is a short-runstrategy. Investing in radical innovation requires developing newknowledge. It is a long-run strategy.

It is important to note that every product is a member of many productcategories. A product presents a group of n characteristics to themarket. The number of combinations of characteristics evaluated by themarket is the number of sets that one can create from n objects. Thisnumber is 2″. Whether a student sees an innovation as incremental orradical, or to what degree in between, depends upon the student'sperspective as well as on the new product. A student with a goodperspective will be able to reduce the risks and costs of innovation.

Finally, it should be noted that by using correlation measures to choosea product value function, an MTS designer can change the relative numberof incremental and radical innovation available for students whendesigning products. As value functions become less correlated, the MTSpresents students with fewer incremental innovations and more radicalinnovations.

Technological Advance:

A technological advance is simulated by restricting and then removingrestrictions on the valid product designs. Depending upon the specificform of the attribute-characteristic representation, this may includerestricting/expanding the domains of the attributes or the number ofattributes. In the preferred embodiment, restrictions on the domains ofattributes in products designs are applied and later removed. After atechnological advance, multitudes of new products and product categoriesbecome available to students. Students compete by exploiting these newopportunities. Metaphorically, after a technological advance studentscan search new areas of the mountain range. Formally, after atechnological advance, students can search new volumes of themultidimensional product space. Students will have to develop newdefinitions of the market (new perspectives) and new definitions oftheir firms' businesses (new product categories). In extreme cases,students will have to ‘reinvent’ their firms. This requires developingnew knowledge while shedding the knowledge made obsolete by thetechnological advance.

Measurability of Information:

One of the important properties of the present invention is thatinformation can be measured. There may be several measures, each usefulfor a different purpose. Two important measures are the reliability ofinformation and population statistics. In the case of qualitativelyvarying attributes the new method is combinatorial and is congruent withthe mathematics of information theory.

To understand the measure of information reliability, consider the taskof product design. In determining product designs and in hypothesizingproduct categories, students might use their knowledge of the value ofproducts in a category to predict the values of products in othercategories. To borrow a real world example, “What do strong sales ofsports cars indicate about the values of utility vehicles?”Alternatively, students might use their knowledge of the value of asingle product to predict the effects of design changes to that product.In either case, the reliability of information measures this predictivecapacity. It is given by the appropriate correlation measure: theautocorrelation function or the correlation between two productcategories.

The measure and usefulness of population statistics can be understood byconsidering the students' task of evaluating a product category. In theterms of statistics, the products that are evaluated in the marketplacecompetition are samples from a population (the products in a productcategory). The students' evaluation of product categories viamarketplace results is similar to a statistician's evaluation of apopulation via sampling. Because of this similarity, populationstatistics apply to analyze how students choose products to send to themarketplace (how students sample the population). In addition,population statistics apply to provide objective measures of the valuesof products in a product category (for example, confidence intervals).These objective measures are compared to a student's subjectiveestimates to identify biases in the student's judgment.

Other Important Properties of Information:

By modeling the product space in accordance with the invention, severalsignificant properties of the information produced include:

1. The marketplace produces an enormous amount of information (asdescribed above).

2. A student does not have enough of the information he desires.Marketplace results only estimate the values of products contained in aproduct category.

3. The firm's budget does not permit exploring all choices. Based uponlimited information, a student can only investigate a few productcategories. This makes success a matter of probability.

4. Product offerings produce both revenue and new information, andgenerally do so in an inverse relationship. That is, products thatgenerate revenue produce little new information and products thatproduce new knowledge initially generate little revenue.

5. For every type of innovation, incremental through radical, previousmarketplace results possess the proper reliability of information.

Explicit Representation of Knowledge:

Product categories and perspectives provide a basis for categorizingproducts and information. In both cases, the categorizations arestructures that embody knowledge.

Product Categories:

Hypothesized product categories determine the innovations a studentmight design (it is the product space where he looks). Theydetermine—from a probabilistic viewpoint—the efficiency of a student'ssearch for valued products. Stronger knowledge (product categoriescontaining higher valued products) permits discovering (1) valuedproducts with less investment or (2) higher valued products given thesame investment. Through product categories, knowledge promotesefficient innovation.

Perspective:

Perspectives select the information from the marketplace database thatevaluates product categories. Comparatively, stronger knowledge (aperspective that includes product categories which differ greatly intheir products' values) can separate high value product categories fromlow value product categories with less information or provide a betterprediction of product categories' values given the same amount ofinformation. Stronger knowledge (1) reduces risk because students havesuperior identification of high value product categories and (2) reducesthe investment needed to find high value product categories. Throughperspectives, knowledge reduces the risks and costs of doing business.

In both cases, stronger knowledge means being able to more efficientlyfocus one's resources to satisfy the marketplace.

Core Competencies:

When searching a product category, a student learns the valuablecombinations of characteristics for that category (the characteristicsthat the ‘#’ attributes shown in FIG. 13 should express). With thisknowledge, the student can efficiently improve his products' designs. Inthe mountain range metaphor, the student is learning the topology of onearea of the mountain range. When this situation exists, the student hasdeveloped a core competency.

One can record the development of a student's core competencies throughstatistical measures (measures of central tendency and variation) of theproducts that the student offers the market. For a set of products, onecan measure a core competency with the vector (A₁, A₂, . . . , A_(n),Var,{overscore (V)}). In this vector A₁, A₂, . . . , A_(n) is anarchetype product. Its characteristics are the characteristicsrepresented most often in the set of products. Specifically, forqualitatively varying attributes, A_(i) is the characteristic expressedmost often by the i^(th) attribute. For quantitatively varyingattributes, A_(i) is the average value of the attribute. The variableVar measures the deviations of the actual products from the archetype.For quantitatively varying attributes, one can measure these deviationswith a calculation of variance. In the qualitative case, one must firstquantify the deviations. One can accomplish this with the concept ofHamming distance. The Hamming distance between two products is equal tothe number of characteristics by which the products differ. For example,the Hamming distance between products (QWERTYUIOP) and (QWERTYUMNB) isthree. In the case of qualitatively varying attributes, the variable Varis equal to the average of the Hamming distances between products in theset and the archetype product. The variable {overscore (V)} representsthe average value of the products in the set.

One can apply this measure of core competency to any set of products(for example, the products produced in a project, by a firm, or by allfirms in a round of the learning session). By repeating this calculationover several rounds, one can track the evolution of core competencies.FIG. 14 depicts this application. The horizontal axis indicates theround. The vertical axis indicates amount by which an archetype productdiffers from the archetype product in round one (the Hamming distance inthe case of qualitatively varying attributes). The figures progressingacross the graph represent the core competency measure (A₁, A₂, . . . ,A_(n), Var, {overscore (V)}). The center bar of the figure representsthe archetype. The span of the figures represents the variation inproducts, Var. Above each figure along the top of the graph is theaverage value of the set of products, {overscore (V)}.

FIG. 14 shows considerable movement in the development of a corecompetency. The large change in archetype between periods three and foursuggests that the student has changed his focus to a new productcategory. The decrease in variation after round five indicates that thestudent has begun focusing upon production rather than search. A studentwould do this when he finds high value products. The chart can also beconsidered a learning curve for learning to produce high valuedproducts.

Industry Life Cycles:

Economists and technological historians have researched the birth,development, and demise of industries. They found that most industriesdevelop through a three stage pattern called an industry life cycle (theautomobile, commercial aircraft, and the minicomputer industries arejust a few examples). James Utterback's book Mastering the Dynamics ofInnovation (Boston: Harvard Business School Press, 1994) describesindustry life cycles in detail (see chapter 4). As a brief review,industry life cycles progress as follows:

1. Fluid Stage: An industry begins in the fluid stage. Many new firmsenter and the number of firms operating in the industry increases.Radical product innovation and diverse product designs abound. Marketshare and profits change unpredictably. The profit margins of successfulproducts are large. Technical and marketplace uncertainty are pervasive.The market's previous results poorly predict the industry's development.

2. Transition Stage: As an industry develops, uncertainty decreases andthe industry enters the transition stage. Technologies and applicationsstabilize and product standards emerge. Incremental innovation becomesmore important. The industry consolidates as waves of business failuresand mergers sweep the industry. Only a handful of firms survive.

3. Stable Stage: Eventually an industry enters the stable stage. Marketshares are relatively fixed. Innovations are incremental. Standardmarketing, finance, and management analyses identify market changes,guide strategic planning, and predict the consequences of a firm'sactions. Competition is over price, profit margins are slim, and pricesreflect production efficiency. (Prior art MTSs are useful for simulatingmarkets in the stable stage, but not in the fluid or transition stages).The industry remains in the stable stage until a technologicalbreakthrough initiates a new life cycle.

In accordance with the invention, a technological breakthrough caninitiate an industry life cycle when either the domains of theattributes or the number of attributes is increased. Initially, the mostvalued products that firms can produce are restricted to havesufficiently low values. The students compete with these choices.Innovation will decrease as students find the most valued products inthis limited set. Once this occurs, the domains of one or moreattributes or the number of attributes is increased, thereby simulatinga technological advance. This will initiate an industry life cycle.

Two Improvements in MTSs:

1. An MTS with Superior Modeling of Competitive Industries

In a competitive industry, MTS students guide a firm through atransition from a predecessor industry to a new industry. Such atransition simulates a technological breakthrough and concommitantdisplacement of an older technology (for example, the transistordestroying the market for vacuum tubes). By producing and usingknowledge, students construct and adjust a portfolio of projects.Students must (1) define the market as the new industry develops; (2)build new core competencies and design new products; (3) protect againstboth short-run and long-run competitive threats; and (4) developmanagerial rules appropriate for the industry's maturity (fluid,transition, and stable stages). Among other lessons gained by engagingin the simulation of a technological breakthrough, students learn thefollowing:

The Management of Innovation:

The process of innovation is unique among business functions. Fordescriptions of the characteristics of innovation and guidance onmanaging innovation see: Peter Drucker, Management: Tasks,Responsibilities, Practices (New York: Harper Collins, 1973) p. 782-803;Peter Drucker, Innovation and Entrepreneurship (New York Harper & Row,1985) p. 143-176; Donald Frey, “The New Dynamism (Part 1),” Interfaces,vol. 24 no. 2 (March-April 1994): pp. 87-91; James Brian Quinn,“Managing Innovation: Controlled Chaos,” Harvard Business Review(May-June 1985): pp. 73-84; and Lowell Steele, Managing Technology: TheStrategic View (New York: McGraw-Hill, 1989): pp. 263-288. The moststartling characteristic of innovation is its unpredictability. Thesuccessful application and design of a radical innovation, for example,is rarely predictable at the start of its development. Thisunpredictability is the source of four other principle characteristicsof radical innovations:

1. High Failure Rate:

Even with proper management, only a small fraction of innovative ideasbecome innovations.

In the present invention, the fraction of successful innovations(successful new product designs) will vary with the type of innovationthat a student pursues, the fraction of products with values higher thanthe products previously sent to the marketplace, and the intensity ofcompetition in a learning session.

2. Path-Dependency:

Innovation is path-dependent. Path-dependency means that (1) some pathsof change will not get from state A to state B while others will and (2)the actions one takes today determine the choices one faces tomorrow(history matters).

The mountain range metaphor provides a striking display ofpath-dependency in the present invention. A student's sequence ofproduct designs produces a ‘path’ winding across the product space. Theknowledge a student develops and the direction he ‘travels’ depend uponthe path that has been previously traversed (history matters). Moreover,the portion of the product space's topology that is correlated with astudents knowledge need not contain products that are competitive in themarketplace (not all paths lead to success). Techniques such as thepreviously described method of measuring core competencies provide meansof measuring and displaying the ‘paths’ of students' productdevelopment.

3. Surprise:

Along the path to success, or failure, lie unpredictable obstacles andbeneficial ‘tail winds.’ These events surprise management.

In the current invention, frustration produces this quality. Attributesthat a student does not focus upon in his marketplace research can behighly interactive. A change in one of these attributes, in a student'sor competitor's product designs, will significantly affect product valueand marketplace competition. Since the student is not focusing upon theattribute, these results will be surprising to the student.

4. Probabilistic Success:

When making decisions, there are always more options than resources.Compounding this difficulty, there is never enough information toconfidently determine the best options. This situation makes success amatter of probability.

In the current invention, a firm's budget will not facilitate samplingfrom all of the product categories that the student deems potentiallyprofitable. In addition, marketplace results only estimate the values ofproducts contained in a product category. Because of this, a studentdoes not have enough of the information he desires.

Because the present invention reproduces each of these properties,students using an MTS in accordance with this aspect of the inventioncan experience and learn the characteristics of innovation. Students canalso learn rules for managing innovation. At a most general level, therules for managing innovation are as follows:

Market Focus:

To be successful, an innovation must make an impact outside of the firm.It must affect a market.

Pursue Multiple Projects:

At the start, each project looks equally inviting (or crazy), yet fewsucceed. To reduce one's risks, one must invest in multiple projects.Match Investment to Knowledge: To further reduce risk, one should startan innovation project with small investments and only increaseinvestment as uncertainty is reduced and information becomes morereliable.

Aim High:

The successful innovation must pay for itself and several failures. Incases of substantial technological change, it will also provide thefoundation of a company for many years. It is imperative that allinnovations seek substantial success and aim for market leadership.

Innovation Requires its Own Measures:

The tools used for managing a mature business are unreliable whenapplied to innovation. How can one calculate NPV when the design andapplication of an innovation is unpredictable? Moreover, the dynamics ofinnovation differ from that of the mature business. Five percent annualprofit increases are unrealistic. Instead, there will be a period wherethere are no profits and, if successful, an ensuing period of rapidlyincreasing profits. Instead of using the accounting and control measuresof mature businesses, one manages innovation projects throughexpectations and feedback. Expectations can always be defined and usedto direct efforts - even when forecasting is unreliable.

Manage Innovation Separately from the Mature Business:

When compared to the mature business, innovation projects appearinconsequential. They produce little or no revenue. Results and problemsin these projects do not immediately affect a firm's performance. Thoughtheir immediate results do not impact the firm, innovation projectsrequire valuable resources. For this reason, managers may not dedicateenough resources to the innovative project. For all these reasons,innovative projects must be managed separately from the mature business.

These rules contribute to success, but they are not sufficient.Implementing these rules requires judgment. Managers must determinewhich projects should be started, which, and when, projects should becancelled, and determine when the firm should adjust its investment in aproject. Managers must set aspiration levels; balance the risk offalling victim to a competitor's innovation with the risk of losingtheir investments in innovation; and negotiate the trade-off betweenflexibility and decision errors. With the present invention studentsdevelop this judgment as they face these dilemmas in the MTS.

In learning these lessons, students will also confront and learn tomanage, by interacting with the business situation through the userinterface, the following issues:

Balancing the Risks of Lost Investment and Lost Opportunity:

The risks of lost investment and lost opportunity are antithetical, asare the costs of their associated mistakes. How should a firm balancethe current and future needs of the business? How does a firm maintainefficiency while also maintaining the flexibility that competitionrequires of the firm?

Managing in a Dynamic Industry:

How much can a firm affect an industry's dynamics? How does one compareresults to expectations when much of this analysis rests upon judgment?How does one evaluate a firm's wealth producing potential?

Portfolio Management:

How many projects should a business pursue? How much diversity isadvantageous, and how does diversity link to core competencies? What aregood measures of innovative performance? How well do traditionalfinancial calculations govern (for example, payback period, NPV, andROI)?

Managing Change:

How fast can a firm change its operating rules, core competencies, andproduct mix without endangering its survival? What kind of rules andmeasures result in change rather than stability? What rules effectivelymove resources from old opportunities to new ones? At what level ofdetail should one plan?

2. Personalized Decision Analysis and Training

Prior art MTSs teach through an indirect method. A student tries variousstrategies, analyzes the results, and, hopefully, the MTS induces animproved understanding. This method of learning can be ineffectivebecause a student learns only as well as he can invent strategies andinduce lessons. In contrast, a direct method of teaching in accordancewith another aspect of the invention analyzes a student's decisions andjudgments in order to determiner his unique, habitual judgment anddecision strengths, errors, and biases. This cognitive analysisfacilitates personalized training in critical thinking and businessdecision-making.

Potentially, MTSs are the ideal means of providing cognitive analysisand training. They present a student with well-defined problems andinformation that results in the receipt of well-defined answers. Whilenecessary, these characteristics are insufficient. In order to providepersonalized decision and judgment analysis, MTSs must meet twoadditional requirements. First, their design must facilitate measuringinformation and knowledge. Second, they must clearly relate the tasksdemanded of students to cognitive functions that can be analyzed. Priorart MTS do not satisfy either of these two additional criteria. Thepresent invention satisfies both of them.

The present invention's means of measuring information and knowledge wasdescribed above. The present invention also clearly relates the tasksthat it demands of students to cognitive functions. To understand therelationships, it is useful to recognize that in designing productsstudents are actually competing to solve a complex optimization problem.Instead of using a scientist's powerful mathematical algorithms for thistask, students use their own ‘cognitive’ algorithms. In doing so,students exercise three cognitive functions: covariation assessment,categorization, and judgment. The relationships between the students'tasks and these cognitive functions are described below.

Covariation Assessment:

When students analyze the marketplace data, they are searching forcorrelations between combinations of product characteristics (productcategories) and marketplace success. In cognitive psychology, thisprocess is called covariation assessment. Experiments have testedpeoples' covariation assessment in a variety of situations.

In one such experiment, subjects were shown several lists of pairedvariables and asked to estimate the correlation demonstrated in eachlist. Dennis Jennings, Teresa Amabile, and Lee Ross, “InformalCovariation Assessment: Data-Based versus Theory-Based Judgment,” inJudgment Under Uncertainty: Heuristics and Biases, edited by DanielKahneman, Paul Slovic, and Amos Tversky (New York: Cambridge UniversityPress, 1982): pp. 211-230. This task is similar to students' analysis ofmarketplace information in the present invention. In the presentinvention, the paired variables are products and sales volume. Thepsychological experiment shows dramatic results. Subjects' estimatesvary widely and, on average, greatly underestimate correlation.Correlations must be at least 0.8 before subjects, on average, estimatea correlation as high as 0.5. These results occur because subjectssimplify their task by looking at only a few entries on the list.Correlation is a quality of the entire set, and only exceptional rowsaccurately convey this quality.

This study suggests that when market results do not make facts obvious,managers can be easily mislead by focusing their attention on a smallset of information (for example, the striking success, the strikingfailure, firsthand experience, or benchmarking). With the presentinvention, this error can be recognized by the system, explained to thestudent in a report or other output, and corrected by the student tobetter avoid real-world errors. When market uncertainty exists, managersshould rely more heavily upon decision rules and conduct a broadassessment of their firms' industry.

Another method that students might use to identify profitable productcategories is to count the number of successful and unsuccessfulproducts in a category. Psychological studies have also researched thismethod of correlation. Dennis Jennings, Teresa Amabile, and Lee Ross,“Informal Covariation Assessment: Data-Based versus Theory-BasedJudgments,” in Judgment Under Uncertainty: Heuristics and Biases, editedby Daniel Kahneman, Paul Slovic, and Amos Tversky (New York: CambridgeUniversity Press, 1982): pp. 211-230. With this method, the informationavailable to students can be placed in 2×2 matrix, as shown in FIG. 15.(In keeping with the preferred embodiment, FIG. 15 shows a product classfor the case of qualitatively varying attributes. FIG. 15 could easilybe expanded to also illustrate other types of attributes). Whenassessing correlation from these types of tables, people typically useonly a fraction of the information in the table. Most people either lookat the number of counts in the upper left-hand quadrant (the yes-yesquadrant) or look at the counts in the top row. These two strategies canproduce error. A proper assessment of the correlation requires using theinformation in all four quadrants of the contingency table (for example,comparing the fraction of successful products that are members of acategory to the fraction of successful products that are not members ofthe category). By outputting contingency tables for the student to useand review, the present invention can teach students to use all of theproduct category information available to them (given theirperspective). It can also illustrate the decision errors and theconsequences of these errors that arise from using only partialinformation through the contingency table, shown above or in combinationwith a report or other output.

Categorization:

Categorization is a technique commonly used by people to simplify theirenvironment. This is exactly what a student does when he hypothesizesproduct categories and a perspective (defining his business and themarket). A student's categorization will have a dramatic affect on hisperformance. To see this, suppose that each student associates eachproject within his portfolio with an estimate of its potential forproducing profits. This estimate can be represented as a probability andupdated each round. Different categorizations will incorporatemarketplace results differently. Because of this, students' expectationswill evolve differently even though they view the same marketplaceresults. This will lead to different assessments of opportunities andrisks and different actions. With the present invention, one can analyzehow students form and change their categorization schemes by trackingthe product categories and perspective used by the student in each roundand how students' categorizations and other decision-making choicesaffect their management decisions.

Judgment:

During the course of a learning session a student must make thefollowing project management judgments: the value of the products in aproduct category; the costs and time required to find valuable products;and the reliability of information. The student must also judge hisportfolio's risk, capital requirements, and potential for producingprofitable returns. Finally, a student must also assess his level ofconfidence in his judgments. Each of these judgments can be input intothe model through the user interface.

In each round of a learning session, one can solicit each of thesejudgments from a student. Furthermore, for each of these judgments, onecan estimate the true value by calculating correlations between productcategories and utilizing population statistics. From these values, theMTS administrator can identify which of the student's judgments arehabitually erroneous. The administrator can also investigate how theseerrors affect a student's project and portfolio management. With asuitable definition of risk, one can perform an analogous analysis of astudent's risk management.

Judgment analysis can address both a student's decision making and theimpact of his decisions on his firm. It can address the followingquestions:

How does the manager recognize and account for uncertainty, informationof varying reliability, surprises and errors, and variation inperformance?

How do knowledge, information, risk, and competition influence themanager's aspiration levels; assessment of opportunity, risk, andpotential returns; and allocation of resources?

Are the manager's aspiration levels and resource allocations consistent?What causes convergence or divergence of aspirations, expectations, andactions?

How do the manager's decisions and judgments influence his business'scapital requirements, risk, return, and adaptability?

Does the manager correctly judge his firm's influence on its industry?

In addition to addressing these questions, an advanced judgment analysisidentifies and corrects errors which are typical human thinking, such asbiases from anchoring, overconfidence, honoring sunk costs, and scenariothinking. For a description of these biases see the appropriate chaptersof: Robyn Dawes, Rational Choice in an Uncertain World (USA: HarcourtBrace Jovanovich, 1988); and Daniel Kahneman, Paul Slovic, and AmosTversky, editors, Judgment Under Uncertainty: Heuristics and Biases (NewYork: Cambridge University Press, 1982).

The following two examples illustrate decision errors that an MTS inaccordance with the invention identifies and corrects:

The Error of Overestimating the Likelihood of Contingent Events:

Suppose that developing a new core competency requires developing newknowledge in four stages, each stage developing upon the previous one.Specifically, a student using the present invention will first discovera good product category and then ‘fine tune’ the product design in threestages. Each stage will identify good characteristics for the ‘#’attributes.

Suppose that the firm has a 75% chance of successfully completing eachstep of the task. The firm has approximately a 32% chance of success(0.75⁴=0.316). Because the firm will discover the combinations insuccession, one can treat these discoveries as independent, conditionalprobabilities. Let A, B, C, and D stand for the first, second, third,and fourth discovery of valuable combinations of characteristics. Theprobability of success is therefore prob(D)=p(A)*p(B\A)*p(C\B)*p(D\C).

Psychological studies of anchoring suggest that people overestimate thechance of success by as much as about 70%. This overconfidence canimpact a firm by causing its managers to (1) bet on too few projectsrather than building a diversified portfolio and (2) invest in projectslong after development suggests that failure is nearly unavoidable.

The Influence of Sunk Costs on Judgment:

All firms face two antithetical risks: lost opportunities (thatcompetitors might exploit) and lost investment. Technological and marketopportunities and competitors' strategies determine which riskdominates. In an effort to justify and honor previous, unrecoverablefinancial and psychological commitments (sunk costs), a manager mightresort to adverse behavior, including (1) decreasing his estimate of therisk associated with previous commitments; (2) increasing his estimateof the benefits of previous commitments; and (3) utilizing selectiveattention. (Selective attention highlights information that supportsone's position while dismissing contrary evidence). These effectspromote resistance to change. Executives forgo profitable opportunitiesand unknowingly expose their firms to excessive risk.

Other Applications

Those skilled in the art will appreciate the many variations of the MTSof the preferred embodiment. Though each variation requires some changesto the system described above, each such construction and operation isfundamentally the same. Some of these variations include:

1. For some multipeaked functions, one can use charts and/or acalculator. This makes possible the use of the present invention inbusiness simulation board games.

2. One can enlarge the system presented above by: (1) includingadditional product traits (for example, business process traits); (2)having the computer simulate competitors; (3) modeling sophisticatedfinancial markets, manufacturing, marketing, and/or accounting; (4)and/or by including supply curves for capacity and/or productcharacteristics.

3. The object designed by students and sent to the marketplace need notbe a product. For example, it could be an advertisement which is sent tothe market and whose success is then gauged. FIG. 16 provides anillustration of such an object, having n qualitative attributes. FIG. 17illustrates a block diagram of a simulated competitive industry fortesting designs of objects in general.

4. In addition to expanding the valid object designs (simulating atechnological advance), one can restrict the valid object designs inorder to represent shortages of component parts or governmentregulations.

5. One can include market disturbances by letting a portion of theproduct attributes represent factors influencing the market. Students donot view these attributes. By intermittently changing thecharacteristics expressed by these attributes, one simulates shocks tothe marketplace. Shocks can range in ‘size’ from incremental to radical.Students must adjust their product designs and business strategies inresponse to these shocks, which have the effect of deforming thetopology of the product space that students search. This means thatstudent's are searching a changing value function. This application issignificant because it shows that the present invention also applies tovalue functions that change throughout the simulation (for example, inresponse to changes in macroeconomic parameters, consumer tastes, orother shocks to the simulated industry).

6. A portion of the attributes can describe products while anotherportion describes customers' applications of a product (call theseattributes application attributes). Interactions between productattributes and application attributes represent the effect of productchanges on the application and the effect of using an existing productin a new application. Several variations arise from this formulation.

6.1. Customer groups seeking products for different applications providea means of representing distinct market segments.

6.2. The MTS can intermittently change the characteristics expressed bythe application attributes. This simulates changes in customer needs.Students must adapt their firm's products to these changes. Changes canvary in ‘size’ from incremental to radical. These changes can bedetermined by a program in the MTS or by the MTS simulating customerswho ‘autonomously’ develop new applications.

6.3. Students can search for both new products and new applications forproducts. Students will manage both product research and marketdevelopment.

6.4. By combining elements of the enhancement just described (marketsegmentation, developing new applications), MTSs can more realisticallysimulate industry life cycles. For example, after a technologicalbreakthrough, firms often must invent applications and educate customersto the benefits of these possibilities.

7. One can let several students control a firm and divide a product'sattributes into several groups (for example, attributes one throughfive, six through ten, and eleven through fifteen). Each student designsone segment. Interactions between attributes represent the impact of onestudent's decisions on other student's design. To design high valueproducts, students must coordinate their efforts and work as a team.

8. The present invention can teach coordination and teamwork by having ateam of students control a firm. Some students set ‘corporate’ strategy(design the general characteristics of the firm's portfolio anddetermine funding for projects) while other students propose projectsand design products. To teach the importance of information andcommunication, one can limit the communication between the two groups.

9. One can let several firms develop a product, each determiningdifferent attributes. This arrangement offers several MTS possibilitiesincluding the following:

9.1. Different firms control different functions involved in bringing aproduct to the marketplace. For example, one firm determines theattributes describing a product's design, another determines theattributes describing advertising, and a third determines the attributesdescribing sales strategy.

9.2. Groups of attributes can represent components of a product (whichmight also be divided into components). Each firm makes one component.In efforts to produce the final product, firms must either purchaseother components, form alliances, or diversify their manufacturing.

The above detailed description of the invention, preferred embodiment,two improvements, and other applications section, describe incorporatinga new method of representing innovation, a new method for producingproperties of information and knowledge, and a new method of makinginformation and knowledge measurable in competitive industry MTSs. Thoseversed in the art will recognize that one can incorporate theseinventions in a variety of MTSs including the ‘general case’ MTSdepicted by FIG. 18. By incorporating the attribute-characteristicrepresentation and the multipeaked value function into ‘general case’MTSs, these MTSs will gain the properties of information and knowledgeand the direct association of decision making with cognitive processes.

FIG. 18 depicts a ‘general case’ MTS of the present invention. Studentsparticipate in a simulated business situation 1801. Students receiveinformation about the business situation via a display 1805. Based ontheir assessment of this information, students design objects. Theobjects are represented with an attribute-characteristic representation.Students input their object designs into the simulated businesssituation through an input device 1806. The simulated business situationevaluates the objects with a multipeaked value function 1803. A businesssituation manipulator 1802 takes the objects' values and calculates andobjects' effects on the business situation. The results of the effectsare displayed to the students through a display device 1805. During alearning session, if desired, a design restrainer 1804 can restrictand/or expand the range of valid object designs.

This arrangement depicted in FIG. 18 can apply to a wide variety ofbusiness situations. These possibilities include:

1. The simulated business situation may include a simulated factory. Inthis case, the object desings are machines. The object values are themachines' capital to output ratios (or capital to labor ratios).Students design machines in an effort to invent more efficient machinesand decrease manufacturing costs. The business situation itself can besimulated with two methods. One can simulate the factory with a set ofequations. For an example of simulating production with equations see:Steven Gold and Thomas Pray, “The Production Frontier: ModelingProduction in Computerized Business Simulations,” Simulation and Games,vol. 20 (September 1989): pp.300-318. Alternatively, one could simulatethe factory with one of the many software packages made for simulatingfactories and production lines.

2. The simulated business situation is a competition between designteams. Students design a product. Given a predetermined number oftrials, students compete to develop the best design. In this case, thereis no market. Instead, the display shows students their designedproducts and the associated product values.

FIG. 19 presents another MTS based upon representing objects with anattribute-characteristic representation and evaluating objects with amultipeaked value function. This is the case of an auction. In thesimulation of an auction, the ‘auctioneer’ 1901 creates objects 1902 andevaluates these objects with a multipeaked value function 1903. Thedisplay 1905 shows students the generated objects. Students bid forthese objects, with the highest bid receiving the value of the object.The goal of students is to accumulate the most value. If desired, onecan provide a design restrainer 1904 for restricting and expanding thevalid object designs.

With reference now to FIG. 20, a hardware arrangement 2000 isillustrated including a central computer 2010 which is preferablyconfigured to run a program which implements the system and method ofthe present invention. An instructor or leader responsible for runningthe simulation connects to the central computer through a main station2020, for example, using a personal computer having a graphicalinterface suitable for entering the various inputs and displaying theoutputs of the system.

The main station 2020 communicates with the central computer 2010 via acommunication link 2030, for example, a modem or dedicated communicationline. A plurality of stations 2040 are also connected to the centralcomputer through respective communication lines 2050. Each station 2040preferably comprises a personal or laptop computer such as one owned bythe participant in the simulation. Preferably, each firm in thesimulation (e.g., management training simulation) is controlled at onestation 2040; however, a single station or user can control pluralfirms, or multiple stations can share responsibility for governing theactivities of a single firm within the spirit of the present invention.The hardware arrangement 2000 of FIG. 20 illustrates a preferredarrangement in which each firm [x] is controlled by a respective station[x].

In accordance with one aspect of the present invention, the MTS canautomatically evaluate the performance of a particular student's designor designs relative to predetermined criteria such as other studentdesigns or bench mark levels established statistically or otherwise.With reference now to FIG. 21, a process flow for evaluating a student'sdesign is described. The evaluation analyzes judgments made by thestudent as reflected in their object designs and changes from round toround made by the student as reflected in their object designs andchanges from round to round in view of the information they obtain fromthe database and the filters they used. During the course of thesimulation, the participant makes project management judgments,including, but not limited to, the value of the products in a productclass; the costs and time required to find valuable products; thereliability of information; and his level of confidence in hisjudgments. They system monitors these judgments by analyzing formssubmitted electronically. Each round of the simulation, one can soliciteach of these judgments from a student. Furthermore, for each of thesejudgments the simulation can estimate the true value by samplingproducts and calculating correlations. For these values, the simulationadministrator can identify which of the student's judgments areerroneous.

At step 2100, the system obtains an attribute-characteristicrepresentation of one or more designs from each of the studentsparticipating in the simulation. Such designs are obtained by completinga form that preferably is presented electronically on the display screenat the stations 2040. For example, a user interacts with the variousfields displayed through the interface illustrated in FIG. 12 andadjusts product design and capacity by submitting to the centralcomputer (e.g., posting) his or her production decisions using thebutton 1204. On the server side, the form from the station 2040 isfunneled to a cgi-bin or the like and processed by a conventional formprocessing software.

The central computer 2010 evaluates the designs posted by the studentsusing a multipeaked value function, as at step 2110. The centralcomputer outputs at step 2120 marketplace performance data to eachstudent with respect to their respective designs. The marketplaceperformance data is communicated from the central computer over thecommunication lines 2050 to the stations 2040 and, more particularly, tothe station which posted that particular design in the first place.Meanwhile, the designs of all students can be provided acrosscommunication line 2030 to the main station 2020 so that the instructorcan review and monitor progress in the designs as the simulationproceeds. The central computer records in a memory each of the designsit obtains from the various stations 2040 along with the value computedby the multipeaked value function for the present round, as at step2130.

Next, a determination is made as to whether the simulation is tocontinue, at step 2140. This simulation can continue for a predeterminednumber of rounds or until other predetermined criteria are satisfied.For example, a simulation may continue (1) until a set number of firmshas gone bankrupt, (2) a certain number of rounds after a radicalinnovation was introduced into the marketplace, or (3) based on othercriteria. In the event that the simulation is to continue, then,optionally, the multipeaked value function and/or the number ofattributes and/or the domains of one or more attributes can be alteredto simulate exogenous shifts in the marketplace. For example, when themultipeaked value function is altered, then the values for all objectsin the simulation are affected. As another example, when the number ofattributes and/or the domains of one or more attributes are altered, thesimulation models the discovery of the new product, a shortage of rawmaterials, or government regulation.

At step 2160, the system obtains at step 2160 revised designs from thestudents along with other data respecting the simulation such asrequests for reports, surveys, advertising budgets, budget allocations,revised production schedules, royalty payments, and the like. Thisinformation is obtained by posting a form as described above inconnection with step 2100. The process flow then loops back to step 2110and repeats so that the revised designs can be evaluated using the(optionally altered) multipeaked value function, with the results beingoutput to the students and recorded at the central station.

In the event that the simulation is not to continue further, as testedat step 2140, the process flow instead branches to step 2180 at whichstep each student's design is automatically evaluated relative topredetermined criteria, as stated above.

Turning now to FIG. 22, a process flow for developing thedecision-making skills of a user or for representing changes in designopportunities is illustrated. At step 2200, a simulation such as amanagement training simulation is defined which includes an attributecharacteristic representation for designs (e.g., products) and amultipeaked value function which is used for evaluating the design. In apreferred application, the designs are products that are to be sent tomarket by competing firms in a competitive business simulation.

At step 2202, the simulation and firms are initialized, that is, thestarting settings for firm [1], firm [2], . . . , firm [n] areestablished. The initial set up of a firm can be as shown in FIG. 12 inwhich there is an existing budget and existing products and capacity, orthe companies can start with no products or capacity and thereafterchoose which products to make within the rules of the simulation. Atstep 2204, the system generates a round of marketplace data using themultipeaked value function. At step 2206, the system (e.g., centralcomputer 2010) provides each firm (e.g., station 2040) currentstate_of_firm data.

In accordance with a salient aspect of the present invention, thecentral computer receives from each station 2040 a filter setting whichis used to guide the retrieval of marketplace data from the memory ofthe central computer. A filter se n be provided by the participant bycompleting a form that includes the same type of information that isavailable through the marketplace data display shown in FIG. 11.

Briefly, with reference to FIG. 23, using the preferred embodiment as anexample, a form for providing search queries of the marketplace isillustrated. First and second filters are illustrated, along with asubmit button. The first filter includes product characteristics “QWE”.A second filter includes the search criteria “>10” under the columnlabeled “Units Sold”. The user can submit one or both of these filtersto the central computer for accessing a limited set of data from asubstantially larger database of marketplace performance data onparticular product designs. If the marketplace data of FIG. 11 were theonly data in the marketplace and the filters of FIG. 23 were applied tothat database, several different data looks could result. If only filter1 were used, then the marketplace data would return the information fromthe first and third rows of FIG. 11 because those two rows include theproduct characteristic string “QWE” in columns 2, 3, and 4,respectively. On the other hand, were filter 2 used against the data inFIG. 11, then the data in rows one and two would be obtained because thenumber of units sold in those two rows exceed 10. Thus, a studententering in the first filter would obtain different information aboutthe product space than a student who entered in filter two, and astudent who entered in filters one and two would obtain yet a differentset of information about the marketplace. Marketplace performance datathat can be obtained includes, but is not limited to: the number ofunits that were sold in the marketplace, the market share, the marketranking, and price information. The data retrieved from the marketplacedata preferably requires an expenditure from the firm's budget, and theuser must decide how much to spend for marketplace performance data.Thus, for example, each filter may be associated with a separate charge,or each interrogation of the marketplace performance data may have a setcharge.

Provided that the firm submitting the filter setting has sufficientfunds (see budget 1201), then it will be provided with marketplace datain accordance with that filter setting at step 2210. Thereafter, adetermination is made at step 2212 as to whether the simulation is tocontinue, substantially as descried above in connection with step 2140of FIG. 21. If the simulation has reached its conclusion, then theprocess flow ends as at step 2214. Otherwise, the simulation continuesat step 2216 by determining whether the domain of theattribute-characteristic representation is to be updated. Updates to thedomain include either a change in the number of attributes, a change inthe domains of one or more attributes, or both. Any such change, whichis effected at step 2218, causes the product space to either be expandedor contracted, depending on whether the relevant parameter is beingincreased or decreased.

Regardless of whether the domain is updated, the process continues atstep 2220 by obtaining from the user revised designs, production data,and other input from each firm, that is, from use station 2040. At step2222, a determination is made as to whether a change in the value of anyof the valid designs is appropriate. Such a change can reflect, forexample, an exogenous shift in the product space such as a change inconsumer preferences or inflation. In accordance with a feature of theinvention, the central computer 2010 includes system-set attributes inthe attribute-characteristic representation of the set of valid objectdesigns. The system-set attributes can exhibit strong frustration withother attributes that are alterable by the user. When the system-setattributes are changed, the overall value of the product is impacted.This is manifested in the simulation as sudden change in the value ofthe object versus the value prior to the change. If the value is to bechanged, then the system-set attribute is altered, and new values areassigned at step 2224.

Also, regardless of whether there is change in the value, adetermination is next made at step 2226 as to whether the multipeakedvalue function itself is to be changed for a subsequent round. In somesimulations, it may be desirable to utilize a different multipeakedfunction than an initial one, for example, a new function derived fromthe first multipeaked function. In such a simulation, a new function isassigned at step 2228. Regardless of whether such a new function isassigned, if the simulation is to continue, as was determined at step2212, then a new round of marketplace data is generated using thepresent or current multipeaked value function, as described above inconnection with step 2204. The process flow then proceeds as previouslydescribed, round-after-round, until an end of simulation condition issatisfied.

From the foregoing, it should be appreciated that a result of changingthe domain of one or more attributes is that the set of valid designsfor the object is altered. If the domain of a particular attribute isexpanded to include additional characteristics, then the product spaceis likewise expanded. As a specific example, a manufacturing plant mayhave had resources to paint cars blue, red or white. As a result of anew source of paint, however, the plant can now produce cars that areyellow. If the cars at the plant were defined by attributes, one fortransmission (standard or automatic), another for air conditioning (yesor no), and one for color (red, blue or white) then the product spacewould be increased from 12 possible product designs to a total of 16possible product designs by the introduction of the new color.

It should be understood that the set of valid designs can also bechanged by increasing or decreasing the number of attributes in anattribute characteristic representation of the object. For example, theinnovation of air conditioning could be added as a new attribute to anexisting product line. Thus, in the preceding example, if cars ofvarious colors and one of two transmission types comprised the set ofvalid designs, the introduction of the new attribute (air conditioningor no air conditioning) would double the set of valid designs.

The affect of either of these changes is to represent changes in designopportunities in the simulation. Just as the number of attributes or setof characteristics for a given attribute can be increased, they canlikewise be constrained or decreased. Such a change simulates the affectof government regulation, a shortage of supplies, a natural disaster,and the like. In a preferred embodiment of the invention, such changesare automatically initiated by the central server. For example, thedomain of one or more attributes or the number of attributes in theattribute-characteristic representation of the object can be changedautomatically in response to a determination of the amount of innovationin the designs being submitted by participants in the simulation. Thus,if the designs that are being submitted to the system for processing bythe multipeaked value function are approaching maximum values, or notchanging substantially from round-to-round, then the system canautomatically increase either the number of attributes that can be usedto define the objects or introduce new characteristic possibilities thatthe participants can use to improve their respective designs and attemptto capture greater and greater market share.

In the event that the domains or attributes are changed, the value ofeach valid design in the product space preferably remains unchanged. Thevalue of each design would be affected, however, if the multipeakedvalue function were changed or if a system-set characteristic werevaried, that is, if a characteristic is changed by the system ratherthan the user. For example, if the simulation is modeling the beginningof an inflationary period, then a characteristic can be changed by thecentral server to reflect the new level of inflation. If this system-setcharacteristic exhibits strong frustration with other variables in theattribute-characteristic representation, then the overall value of many,if not all, of the designs in the product space will be affected.

A management training system that can be used to implement the method ofthe present invention preferably includes a first computer having aprocessor and a memory and a network connection to a plurality ofstations such as the arrangement shown in FIG. 20. The first computer(e.g., a central server) is configurable to define a simulated businesssituation and to process inputs from the user (e.g., stations 2040)using the multipeaked value function as described above. Each of thestations connected to the first computer executes an applicationsoftware program which preferably permits the user at the station toproduce one or more objects and to submit such objects to the firstcomputer. Each of the objects has a design which is defined usingattribute-characteristic representation. Each of the stations isconnected to the first computer via a connection which permits theinputted object designs to be forwarded to the first computer. Theconnection also permits information concerning the object designs thatare processed at the first computer to be transmitted back from thefirst computer to the plural stations. Preferably, the first computercan identify each of the plural stations and transmit to each specificor particular station information concerning a current state of theuser's designs.

Although the present invention has been fully described by way ofexample with reference to the accompanying drawings, it is to be notedhere that various changes and modifications will be apparent to thoseskilled in the art. Therefore, unless such changes and modificationsdepart from the scope of the present invention, they should be construedas being included therein.

Glossary

Key terms are listed in alphabetical order.

“#”:

The number sign is used to create a compact notation for objectcategories. When displayed as a product attribute, the number signssignifies the entire range of object characteristics.

Aggregate Traits:

Aggregate traits describe qualities of an entire product such as‘quality’, ‘reliability’, ‘durability’, and ‘value’. Aggregate traitsare valued with a numerical scale.

Attributes:

Attributes are the types of features of a product's design such asphysical qualities, components, and abilities. Attributes can varyqualitatively, quantitatively, or in more complex ways (e.g., dualvarying attributes). An attribute is a variable and its characteristics(see below) comprise the domain or set of possible instantiations forthe attribute.

Attribute-characteristic representation:

A method of representing the design of an object as a collection ofattributes. Each attribute expresses one characteristic from a set ofpotential characteristics.

Business Process Traits:

Business process traits describe the qualities of products that arisefrom business processes, such as customer service and delivery delay.

Characteristic (product characteristic):

Characteristics are the instantiation possibililties that an attributecan express. A characteristic of the “color” attribute can be the set of“blue,” “green,”

Competitive Industry MTS:

A competitive industry MTS is an MTS where one or more firms compete ina simulated marketplace.

Core Competency:

A core competency is a strong capability of a firm. They permit a firmto differentiate its products from its competitors' products. Thisdifferentiation can be an important source of competitive advantage andprofit. In the prior art students develop core competencies by heavilyinvesting in a particular product trait. In the new method, studentsdevelop core competencies by discovering product categories with highlyvalued products and learning how these product categories correlate withother product categories.

Correlation:

Correlation is a statistical property relating information about onearea of the product space (a particular design or a product category) toother areas of the product space. It measures how well the values of theproducts in one area can predict the values of the products in otherareas.

Demand Function:

A demand function is an equation or set of equations that receives asits inputs (independent variables) decisions and outputs (e.g., productsand/or advertisements) of a firm and determines sales of that firm'soutputs.

Design Restrainer:

A design restrainer adjusts the domain of product attributes and thenumber of product attributes in order to manipulate the set of validproduct designs. The design retrainer can be an automated routineresponsive to predetermined conditions or rules, or may be a person suchas an administrator or simulation manager.

Distance Between Products:

The distance between product measures the amount that two productsdiffer. For example, for qualitatively varying attributes one might usea count of the number of attributes expressing different characteristicsas a calculation of the distance between the products. Similarly, forexample, for quantitatively vary attributes one might apply themathematical calculation of difference to the attributes displayingdifferent characteristics and then use this measure to calculate thedistance between the products.

Distance Value Function:

A distance value function is a function that assigns value as amonotonically decreasing function of the distance between a product andan ideal product. Distance value functions do not model frustration.They are single peaked.

Demand Elasticity:

The demand elasticity is a number describing the percentage change indemand for one percent change in a factor that influences demand (forexample, the price of a product). Demand elasticities can be calculatedfor industry demand (for example, how an industry's demand varies withthe price of a product) and for firms (for example, how a firm's demandvaries with the price of a product).

Design (Product Design):

The specific characteristics expressed by a product's attributesconstitute a product's design.

Domain:

As used herein, is the set of possible characteristics an attribute canexpress.

Endogenously:

‘Endogenously’ denotes that a property or event arises from the actionsof students using an MTS. For example, the product categories thatstudents focus upon when designing innovations are determined bystudents during the learning session. Because they arise from within theMTS, during a learning session, they are endogenous

Exogenously:

‘Exogenously’ denotes that a property or event is defined by theconstruction of the MTS. For example, in a prior art MTS, therelationship between investment and the probability of an innovation isgiven by the probability distributions built into the MTS. Thisrelationship, therefore, is determined exogenously. In MTSs built withthe new technology, the function relating investment to innovationdepends upon a student's knowledge and decisions. This depends upon thestudent's use of the MTS and evolves throughout the learning session. Innew technology MTSs the relationship between investment and innovationis not exogenous, but endogenous.

Firm:

A firm is a company that competes in the simulated competitive industry.A student learning with an MTS manages a firm. Some MTSs also includefirms managed by the computer.

Frustration and Strong Frustration:

Frustration exists when changing the characteristic expressed by oneattribute (1) increases the contribution that the attribute makes toproduct value while simultaneously (2) decreasing the contribution toproduct value made by other attributes. When the result of frustrationis a decrease in the value of the product, it is called strongfrustration.

Ideal Product:

The ideal product represents the product design most preferred bycustomers. Prior art MTSs use an ideal product to evaluate the designsof products that students send to the marketplace.

Information Reliability:

The reliability of information measures how well information about thevalue of a product or product category predicts the value of otherproducts or product categories. It is given by the appropriatecorrelation measure.

Information Theory:

Information theory is a mathematical investigation of communication thatdefines communication signals and information with mathematicallyrigorous definitions. It is useful for calculating the amount ofinformation produced by a marketplace and the amount of informationutilized by a student.

Interaction:

An interaction occurs when the characteristic expressed by one attributeinfluences how a characteristic by another attribute contributes to aproduct's value. When this occurs, the first attribute interacts withthe second attribute.

Interface:

The interface provides the student with a method of communicating withthe MTS. It translates the simulated business situation results intobusiness language and translates a student's decision into computercode.

Learning Session:

A learning session refers to time during which a student uses an MTS.

Management Training Systems:

Management training systems are computer programs used by students(usually managers and aspiring managers) for learning and practicingmanagement. A management training system simulates a sequence ofrealistic decision situations. The student responds to each situationwith a decision. The management training system calculates the result ofthe decision and displays it for the student.

Market:

A market is a collection of customers that evaluate, and possiblypurchase, the products produced by firms.

Market Manipulator:

A market manipulator is a structure in an MTS that contains the demandfunctions and calculates the sales of products in a simulated marketusing these functions.

Market Segment:

A market segment is a collection of customers within a market who sharea preference for a distinctive set of product traits.

Marketplace:

The marketplace is the part of an MTSs' computer program that determinesthe sales of products. Its simulates a real ‘marketplace’ where firmsand customers meet to trade.

MTS:

MTS is an abbreviation for management training systems.

Multipeaked Valued Function:

A multipeaked is a value function that has multiple optima and has asits domain all products or objects in the simulation.

Perspective:

A perspective is a set of product categories that a student uses toselect information from the marketplace results to use for the purposesof decision making.

Product:

Products have specific instantiations of each attribute in theattribute-characteristic representation. For example, if a₁={0,1},a₂={0,1}, and a₃={0,1} then three examples of products are (001), (101),and (110). Products may take the form of devices, services,advertisements, and other objects that define outputs of a firm. Thevalue obtained by the product evaluator 803 for a given product is usedby the market manipulator 802 to determine a firm's sales of thatproduct. If the product is a device then the sales are the sales of thedevice. If the product is an advertisement, then the sales, of course,are sales of some product or service, the performance of such sales inthe marketplace being a direct reflection of the value of theadvertising campaign.

Product Class:

A product class is the set of products consisting of all possible valuesof a product's traits. A product's traits typically include factors inaddition to attributes as used in the attribute-characteristicrepresentation.

Product Category:

In the present invention, a product category is a set of productsdefined upon the appearance or absence of product characteristics in aproduct's design. Students define product categories throughout alearning session. For example, one product category can be all blueproducts and another can be all non-blue products. In formal terms, aproduct category can be narrowly defined to be coextensive with a singleproduct (e.g., (111) from the example used in the definition of“product” above) and broadly defined to be coextensive with the entireproduct space (e.g., (###) from the example used in the definition of“product” above). Ordinarily, product category is defined between theseextremes.

Product Evaluator:

A product evaluator is a structure in an MTS that evaluates productdesigns.

Product Space:

A product space is a multidimensional space of products with adistribution of product values over this space. For the purpose ofillustration, it is often useful to visualize a product space as in twodimensional space, a Cartesian coordinate system.

Product Traits:

Product traits describe products. There are three types of producttraits: business process traits (describing the outcome of businessprocesses, such as delivery delays), aggregate traits (describing thewhole product, such as quality), and attributes (describing specificfeatures of a product, such as color).

Product Value Function:

This is a function that takes a product's design as its input(independent variable) and determines the product's value.

Project:

A project is a student's exploration and exploitation of the products ina product category hypothesized by the student.

Reinventing a Firm:

Reinventing a business is a term that signifies a firm replacing itscore business with a new business that requires new knowledge. Examplesof firms reinventing their business are IBM switching from electrictypewriters to computers and Motorola switching from car radios tointegrated circuits.

Similarity:

Similarity is a measure of the distance between two products (seedefinition of “distance between two products”).

Student:

A student refers to a person who is using an MTS.

Supply Curve:

The supply curve is a function relating the amount of products producedby an industry to the cost of product inputs, such as labor and rawmaterials.

Topography of the Product Space:

The topography of a product space describes how product values vary overthe products in the product space.

Union of two sets:

The union of two sets is a large set composed of the elements of the twosets.

Valid Product Design:

A product design having a set of attributes within theattribute-characteristic representation, each attribute expressing onecharacteristic from the respective domain of such attribute.

Value (product value):

Value denotes the level of a product's value trait.

Closing Remarks Concerngin The Prior Art Method of Modeling forInnovation and Technological Advance and for Accounting for ProductDesign

Innovation:

In prior art MTSs, innovation either (1) increases the value of anaggregate trait, (2) expand the domain of attributes, (3) increase thenumber of attributes, or (4) adds an entirely new market to the learningsession. Regardless of which effects are included, prior art MTSs use avariation of the following method to simulate innovation: For an examplesee: Thomas Pray and David Methe, “Modeling Radical Changes inTechnology within Strategy-Oriented Business Simulations,” Simulationand Gaming, vol. 22 (March 1991): pp. 19-35. Firms attempt innovation byallocating capital to ‘research and development’. The allocationpurchases a draw from a probability distribution. If the draw exceeds apredetermined threshold, the firm innovates. With this method, studentshave limited influence over the probability distribution and thresholdparameter. The most potent means of influence is investment in researchand development. Greater investment increases the probability ofinnovation.

The prior art method is quite flexible. The frequency of innovation,variation of innovative ability among firms, and firm and industryleaning curves, can all be modeled by adjusting the distribution and thethreshold parameter. These capabilities make the method adequate foradding innovation to MTSs in which innovation is not an important aspectof the MTS (for example, MTSs that teach basic accounting, finance, andmarketing in established markets). While useful in such MTSs, the priorart method of modeling innovation is inadequate for MTSs in whichinnovation is an important aspect (for example, MTSs that teach themanagement of innovation, technological advance, or knowledge). For suchMTSs, the prior art is deficient for several reasons. These are:

1. The samples for the probability distribution simulate the outcome ofthe innovative process. The prior art method does not model theinnovation process itself.

2. The method of sampling from a probability distribution does notrepresent or account for the influence of information, knowledge, ordecision making in the innovation process. All these qualities, and allother qualities of the innovation process, are subsumed within theprobability distribution and threshold parameters.

3. The probability distributions and threshold parameters are definedexogenously by the MTS designer or administrator. These constructs arenot endogenously related to the information produced by the marketplaceor to students' knowledge and decisions.

Technological Advance:

In some prior art MTSs, technological advance is synonymous withinnovation and represented in the same manner. These MTSs suffer fromthe deficiencies listed above. In other prior art MTSs, technologicaladvances are simulated by introducing new probability distributions forrepresenting innovation. This method is also has limitations: It onlysimulates a small number of new opportunities: those specified by thenew probability distributions, whereas real technological advancescreate a multitude of opportunities. Because of this deficiency, priorart MTSs cannot provide students with practice in managing a firmthrough technological change. Moreover, because of this limitation, theprior art MTSs cannot properly simulate the market dynamics that followa technological advance (such as industry life cycles).

The Product Value Function:

The prior art calculates a product's value by comparing each product toan ideal product. The ideal product is the product design that customerslike best. The ideal product is set at the start of a learning session.If there are multiple markets or market segments, each has its own idealproduct. In order to calculate a product's value, the prior art uses anequation that measures a product's similarity to the ideal product. Thisequation quantifies the notion of similarity into a number called thedistance between a product and the ideal product. For this reason, Icall the prior art method a “distance value function”.

For examples of prior art product value functions, see: Richard Teach,“Demand Equations for Business Simulations with Market Segments,”Simulation and Gaming, vol. 21 (December 1990): pp. 423-442 and StevenGold and Thomas Pray, “Technological Change and Intertemporal Movementsin Consumer Preferences in the Design of Computerized BusinessSimulations with Market Segmentation,” Developments in BusinessSimulations and Experiential Exercises, vol. 25 (1998): pp. 156-167.

Utilizing this method, product value is a monotonically decreasingfunction of the distance between two products. Products that have smalldistances (similar to the ideal) have higher values. Products that havelarge distances (dissimilar to the ideal) have lower values. This resultarises because the distance functions do not represent frustration. Theformula for calculating the distance between two points provides anexample. Suppose a product class has four quantitative attributes. Inprior art MTSs, the distance between a product and the ideal might becalculated as DISTANCE=(d₁ ²+₂ ²+ . . . +d_(n) ²)^(0.5), whered_(i)=(I_(i)−a_(i)) and I_(i) and a_(i) represent the characteristicsexpressed by the i^(th) attribute for the ideal product and a productproduced by the student. By taking derivatives one can show that whenthe value function decreases monotonically with distance (1) animprovement in design in one dimension can never cause a decrease in thevalue of a product (i.e., no strong frustration) and (2) that the valuefunction has only one optimum, which occurs when d₁=d₂= . . . =d_(n)=0.

The mountain range metaphor helps to illuminate the qualities producedby this method. Because the distance value functions lack frustration,the product space topology of the prior art MTSs are Mount Fuji-like.The topology has a single peak that stands directly over the idealproduct. As products becomes less like the ideal, their design qualitydecreases, producing broad, gradual slopes. This topology, hassignificant consequences, including:

1. The characteristic expressed by an attribute in the ideal productremains the best characteristic for that attribute to express (at leastas good as any other characteristic) regardless of the characteristicsexpressed by other attributes. Because of this, a student can addresseach attribute independently.

2. By making a series of small changes in a product's design, a studentcan produce a sequence of designs such that (1) each subsequent designincreases product quality and (2) the sequence ends with the idealproduct. Moreover, this property holds regardless of the order in whicha student addresses the attributes.

These qualities do not exist in the present invention described in theforegoing specification. The strong frustration exhibited by multipeakedvalue functions prevents students from addressing each attributeindependently. The strong frustration also prevents students fromfinding the optimal product via a sequence of products that (1) differby incremental design changes and (2) produce a monotonically increasingsequence of product values. In the present inventive system and method,continuing improvement in product design eventually must require a moreradical change in product design. Metaphorically, the product spacetopology of the present invention is multipeaked, as opposed to singlepeaked. Improving a product design that resides beneath the peak of asmaller mountain requires a simultaneous change of several productcharacteristics; one must travel to a different, taller mountain.

There is another significant quality of the prior art (and one thatdistinguishes the present system and method from the prior art). Thisdistance value function creates a highly correlated product space.Information about the value of products provides considerableinformation about other products. The Mount Fuji topology illustratesthis property. Once a student discovers the direction of design changesthat ‘climbs’ the mountain, the student knows a great deal about theentire topology of the product space. This differs from the new methodwhere information is correlated over small areas of the mountain range(among sets of incremental innovations) but uncorrelated across largerareas (areas containing radical innovations).

Some makers of prior art MTSs recognize that the prior art methodproduces highly reliable information. As a remedy, some prior art MTSsadd a random error term to the marketplace information (for example, tothe information found in a marketing report). The difference betweenthis modeling of uncertainty and the present invention is significant.The prior art adds informational uncertainty exogenously. In the newmethod, information reliability arises endogenously and produces theproper relationship between information reliability and innovation.Moreover, the problems of the prior art method are compounded by anotherpractice of the prior art: making the reliability of information varywith investment in marketing research. For an example of this, see:Thomas Pray and David Methe, “Modeling Radical Changes in Technologywithin Strategy-Oriented Business Simulations,” Simulation and Gaming,vol. 22 (March 1991): pp. 19-35. More expensive marketing reports havemore reliable information. In these MTSs, the reliability of informationis a function of investment. In the new method, the information'sreliability is, appropriately, a function of innovation and perspective.

Because distance value functions exhibit the qualities justdescribed—(1) addressing the attributes independently; (2) optimizing ofproduct value via a sequence of small changes in design that eachimprove value; and (3) highly correlated—they are suitable only forteaching the management of low uncertainty situations. These situationsinclude, for example, pricing, designing, positioning, and promotingproducts in established markets (i.e., basic marketing). Prior art MTSlargely are not suitable for teaching the management of high uncertaintysituations. These situations include, for example, entrepreneurship,developing new core competencies, developing radical innovations,managing technological change, and reinventing one's business.

Finally, it should be noted that the prior art does not provide a methodfor measuring information and knowledge; relating these measures to theproduct space topology; or relating these measures to students'decisions. Furthermore, the prior art cannot relate the tasks demandedof students to cognitive functions. Because of these limitations, theprior art cannot usefully illuminate and analyze the role of knowledgeand information in students' decisions, marketplace competition, and thesimulated industry's dynamics. Moreover, for the same reasons, the priorart must teach through an indirect method, where students test a varietyof ideas and, hopefully, induce an improved understanding of management.The prior art cannot teach through the direct method previouslydescribed.

What is claimed is:
 1. In a computer implemented system, a method fordeveloping the decision-making skills of a user, comprising the stepsof. a) defining a simulated business situation including a competitivemarketplace, a plurality of firms, and a set of products which arerepresented by an attribute-characteristic representation, eachparticular firm injecting one or more products each having a productdesign into the marketplace under control of a particular user in anattempt to derive revenues from said products; b) selectively providingthe user with marketplace information concerning at least a subset ofsaid set of products and a marketplace performance data of at least saidsubset; c) providing the user with information concerning a currentstate of the firm under that user's control; d) obtaining a revisedproduct design from the user to send to the marketplace; e) processingsaid revised product designs with a multipeaked value function; f)revising the marketplace information in response to the processedproduct designs; and g) repeating steps b) through c) whereby the userobtains the results of sending the revised product design to themarketplace.
 2. The method as in claim 1, including the additional stepof repeating steps b) through f).
 3. The method as in claim 2, whereinsteps b) through f) are repeated for a predetermined number of rounds.4. The method as in claim 2, wherein steps b) through f) are repeateduntil preselected criteria are satisfied.
 5. The method as in claim 1,wherein the step of selectively providing the user with marketplaceinformation includes charging the user for accessing the marketplaceinformation.
 6. The method as in claim 5, wherein the marketplaceinformation is accessed by inputting product characteristics andobtaining at least one marketplace performance data in response thereto.7. The method as in claim 6, wherein the product characteristics includethe characteristics for at least one particular product design.
 8. Themethod as in claim 5, wherein marketplace information is accessed byinputting a marketplace performance data and obtaining productcharacteristics in response thereto.
 9. The method as in claim 8,wherein the product characteristics include the characteristics for atleast one particular product design.
 10. The method as in claim 1,wherein the marketplace performance data of at least one subset includesat least one of the following: the number of units that were sold in themarketplace, the market share, the market ranking, and priceinformation.
 11. The method as in claim 1, wherein the step ofselectively providing the user with marketplace information includesproviding the marketplace information that satisfies a search queryentered by the user.
 12. The method as in claim 1, wherein themultipeaked value function exhibits strong frustration between at leasttwo attributes.
 13. The method as in claim 1, including the additionalstep of changing a first set of valid product designs that can beinjected into the marketplace to a second set of valid product designs.14. The method as in claim 13, herein the changing step occurs aftersteps b) through f) have been repeated: a. a predetermined number oftimes; or b. until preselected criteria are satisfied.
 15. The method asin claim 13, herein the changing step includes the step of expanding thedomain of one or more attributes in the attribute-characteristicrepresentation that the user can vary in order to simulate atechnological advance.
 16. The method as in claim 13, wherein thechanging step includes the step of expanding the number of attributes inthe attribute-characteristic representation that the user can vary inorder to simulate a technological advance.
 17. The method as in claim13, wherein the changing step includes the step of constraining thedomain of one or more attributes in the attribute-characteristicrepresentation that the user can vary in order to simulate governmentregulation or a shortage of raw materials.
 18. The method as in claim13, wherein the changing step includes the step of constraining thenumber of attributes in the attribute-characteristic representation thatthe user can vary in order to simulate government regulation or ashortage of raw materials.
 19. The method as in claim 13, wherein thechanging step is automatically initiated in response to a determinationof the amount of innovation in the processed revised product designs.20. The method as in claim 13, wherein each product design in first setof valid product designs has the same value in the second set of validproduct designs.
 21. The method as in claim 1, including the additionalstep of changing the multipeaked value function in response to apredetermined condition to simulate an exogeneous change in productvaluation.
 22. The method as in claim 1, including the additional stepof changing a product design characteristic which is changeable only bythe system in response to predetermined criteria thereby effecting achange in product valuation.
 23. In a computer implemented simulation, amethod for developing the decision-making skills of a user, comprisingthe steps of: a) defining a simulated situation including at least onefirm and a set of objects which are represented by anattribute-characteristic representation, each firm being under thecontrol of a particular user and supplying to the simulation one or moreobjects each having an object design; b) selectively providing the userwith simulation information concerning at least a subset of said set ofobjects and a valuation of at least said subset; c) providing the userwith information concerning a current state of the firm under thatuser's control; d) obtaining a revised object design from the user tosend to the simulation; e) processing said revised object designs with amultipeaked value function; f) revising the simulation information inresponse to the processed object designs; and g) repeating steps b)through c) whereby the user obtains the results of sending the revisedobject design to the system.